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2022MNRAS.515.2188H__Rorai_et_al._2018_Instance_1

In this work, we follow the method for measuring the IGM thermal state based on Voigt profile decomposition of the Ly α forest (Schaye et al. 1999; Ricotti et al. 2000; McDonald et al. 2001). In this approach, a transmission spectrum is treated as a superposition of multiple discrete Voigt profiles, with each line described by three parameters: redshift zabs, Doppler broadening b, and neutral hydrogen column density $N_{\rm H\, {\small I}}$. By studying the statistical properties of these parameters, i.e. the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution, one can recover the thermal information encoded in the absorption profiles. The majority of past applications of this method constrained the IGM thermal state by fitting the low-b-$N_{\rm H\, {\small I}}$ cutoff of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution (Schaye et al. 1999, 2000; Ricotti et al. 2000; McDonald et al. 2001; Rudie, Steidel & Pettini 2012; Boera et al. 2014; Bolton et al. 2014; Garzilli, Theuns & Schaye 2015, 2020; Hiss et al. 2018; Rorai et al. 2018). The motivation for this approach is that the Ly α lines are broadened by both thermal motion and non-thermal broadening resulting from combinations of Hubble flow, peculiar velocities, and turbulence. By isolating the narrow lines in the Ly α forest that constitutes the lower cutoff in $b-N_{{{{\rm H\, {\small I}}}}{}}$ distributions, of which the line-of-sight component of non-thermal broadening is expected to be zero, the broadening should be purely thermal, thus allowing one to constrain the IGM thermal state. However, this method has three crucial drawbacks. First, the IGM thermal state actually impacts all the lines besides just the narrowest lines. Therefore, by restricting attention to data in the distribution outskirts, this approach throws away information and reduces the sensitivity to the IGM thermal state significantly (Rorai et al. 2018; Hiss et al. 2019). Secondly, in practice, determining the location of the cutoff is vulnerable to systematic effects, such as contamination from the narrow metal lines (Hiss et al. 2018; Rorai et al. 2018). Lastly, the results from this approach critically depend on the choice of low-b cutoff fitting techniques, where different techniques might result in inconsistent T0 and γ measurements (Hiss et al. 2018; Rorai et al. 2018).

2016ApJ...826..117Y__Roux_&_Webb_2009_Instance_2

Traditionally, the Parker transport equation (Parker 1965) was used to model pickup ion acceleration at the SWTS when using a transport theory approach. However, interesting Voyager results, such as strongly fluctuating pitch-angle anisotropies upstream, the detection of highly anisotropic intensity spikes at the SWTS, the average upstream anisotropy peaking at a surprisingly high energy far above the upstream flow energy, and energetic particle spectra with multiple power-law slopes with breaking points in between that are highly variable upstream (Decker et al. 2005, 2008b; Cummings et al. 2006), suggest that transport modeling should be modified in at least two ways. First, the turbulent nature of the magnetic field conditions at the SWTS should be taken into account, and second, a transport theory that applies when particle distributions are highly anisotropic is needed, given that the Parker transport equation only holds for nearly isotropic particle distributions. In response, shock acceleration transport models were developed in recent years based on the numerical solutions of the focused transport equation (Kóta & Jokipii 2004; le Roux et al. 2007; Florinski et al. 2008a, 2008b; le Roux & Webb 2009) to take advantage of the fact that focused transport is not restricted to small pitch-angle anisotropies. This is especially advantageous at lower suprathermal particle energies upstream, where particle distributions can be sporadically strongly anisotropic (Decker et al. 2006), allowing one to model particle injection into diffusive shock acceleration (DSA) naturally at those energies. Furthermore, statistical variations in the observed magnetic field direction near the SWTS were included as a time series to model time variations in the injection and DSA of pickup ions to simulate the highly volatile nature of actual DSA at the SWTS (Florinski et al. 2008a, 2008b; le Roux & Webb 2009; Arthur & le Roux 2013). This focused transport approach should be seen as complementary to more sophisticated self-consistent shock acceleration models based on hybrid codes (Kucharek & Scholer 1995; Giacalone 2005) and particle-in-the-cell models (Scholer et al. 2003; Lembege et al. 2004), but it has the virtue of relative simplicity because different statistical plasma parameters can easily be studied separately and in combination at the SWTS to gain a more clear conceptional understanding of the role of such statistics on pickup acceleration at the SWTS.

2022MNRAS.515.5629D__Dubber_et_al._2021_Instance_1

In the previous sections, we have shown the difficulty in balancing follow-up time with large numbers of unconfirmed candidate companions from single-epoch photometry. This follow-up dilemma was the motivation for designing the K-peak filter: can we optimize the observing time required to determine the nature of a target? One answer is to use a carefully chosen combination of photometric filters. Using only photometry, we can calculate colours that contain information about the type of object being observed, allowing approximate characterization with single-epoch photometry. Past works (e.g. Najita, Tiede & Carr 2000; Allers & Liu 2020) have shown that custom photometric filters can be used to greatly improve the confirmation rate of photometrically selected candidate low-mass brown dwarfs. In previous work (Allers & Liu 2020; Jose et al. 2020; Dubber et al. 2021), we used a custom filter centred on the deep 1.45 µm feature present in YPMOs to distinguish between them and background sources. In the 2–5 µm range covered by NIX, such water features are far less dominant, and there are strong telluric features across some of this range that would make a similar ‘water’ technique difficult to use. Instead, we use the differing spectral shape in K-band of very low mass brown dwarfs when compared to earlier spectral type stars. This can be seen in the sample of spectra shown in Fig. 3. By locating filters at key spectral points for defining the overall shape of the spectra, the extracted colour information can be used for direct characterization. Also shown in Fig. 3 are spectra of well-studied brown dwarfs and exoplanets, discovered via direct imaging: 51 Eri b (Macintosh et al. 2015), β Pictoris b (Lagrange et al. 2010), PSOJ-318 (Liu et al. 2013), G196-3B (Rebolo et al. 1998), and HR 8799d and e (Marois et al. 2008, 2010). References for the spectral data plotted are detailed in the caption of Fig. 3. These spectra demonstrate the general variety in the spectral shapes of objects that have been detected via direct imaging previously, but also the similar features in the highlighted filter windows.

2021MNRAS.507.5882S__Mackereth_et_al._2018_Instance_2

Cosmological hydro dynamical N-body simulations offer another possibility to investigate the origin of the bimodality in the ([Fe/H], [α/Fe]) plane. Earlier simulations, e.g. full N-body simulations by Loebman et al. (2011), Brook et al. (2012) or hybrid simulations in which a semi-analytic chemical evolution was added on top of a cosmological simulation (Minchev, Chiappini & Martig 2013, 2014), were able to show that the thin and thick discs lie along different tracks in the ([Fe/H], [α/Fe]) plane, with the thick disc being old metal poor and rich in [α/Fe] and the thin disc being young, metal-rich and poor in [α/Fe]. They also showed that migration was important to generate the two discs. However, a clear bimodality in the ([Fe/H], [α/Fe]) plane was not seen. In the past few years good progress has been made to improve the spatial resolution as well as the chemical enrichment prescriptions. The bimodality has now been observed in some simulations (Grand et al. 2018; Mackereth et al. 2018; Clarke et al. 2019), and some of the simulations, in addition to the bimodality, also reproduce the basic trends of the ([Fe/H], [α/Fe]) distribution with radius R (Buck 2020; Vincenzo & Kobayashi 2020). Unlike analytical models, such simulations cannot be fine tuned to reproduce the Milky Way data, hence, the focus of these simulations is to qualitatively reproduce the abundance trends seen in the Milky Way, to understand how frequently do we get the bimodality and what is the mechanism for it. However, there is a lack of consensus between the different studies. Clarke et al. (2019) and Buck (2020) suggest that bimodality should be common in disc galaxies, whereas Mackereth et al. (2018) suggest that it is rare. Each simulation suggests slightly different mechanisms for the existence of the bimodality. Clarke et al. (2019) attribute bimodality to vigorous star formation in clumps at high redshift. Grand et al. (2018) suggest two distinct pathways, a centralized starbust pathway induced by mergers and a shrinking gas disc pathway. Buck (2020) suggest that after the formation of the high-[α/Fe] sequence a gas-rich merger dilutes the metallicity of the ISM leading to the formation of the low-[α/Fe] sequence. Mackereth et al. (2018) attribute the bimodality to unusually rapid gas accretion at earlier times, which is also characterized by a short time-scale to convert gas to stars. While some simulations clearly identify migration as key process to shape the sequences, others do not. In spite of the differences, it seems that some of the simulations (e.g. Mackereth et al. 2018; Buck 2020; Vincenzo & Kobayashi 2020) are not inconsistent with the Schönrich & Binney (2009a) paradigm.

2021ApJ...910...78Z__Condon_et_al._2017_Instance_2

The multiwavelength spectral data of 13 SNRs with hard γ-ray spectra. The γ-ray spectra are fitted with a hadronic model with the normalization of the individual spectrum as free parameters. The model assumes that protons have a single power-law energy distribution with an exponential high-energy cutoff. Note that the TeV spectra of G78.2+2.1 (HAWC) and N132D (HESS) cut off at relatively lower energies, and the soft spectral component of GeV of HESS 1912+101 may be from other contributors and are not considered in SED fitting. The best-fit model parameters are indicated in the figure. References for the observational data are as follows: RX J0852.0−4622: radio (Duncan & Green 2000), GeV (Tanaka et al. 2011), X-ray (Aharonian et al. 2007), TeV (H.E.S.S. Collaboration et al. 2018c); RX J1713.7−3946: radio (Lazendic et al. 2004), X-ray (Tanaka et al. 2008), GeV and TeV (H.E.S.S. Collaboration et al. 2018a); HESS J1731−347: radio (Tian et al. 2008), GeV (Condon et al. 2017; Guo et al. 2018), X-ray (Doroshenko et al. 2017), TeV (H.E.S.S. Collaboration et al. 2011); RCW 86: radio (Clark et al. 1975; Lemoine-Goumard et al. 2012), X-ray (Lemoine-Goumard et al. 2012), GeV (Ajello et al. 2016), TeV (H.E.S.S. Collaboration et al. 2018d); SN 1006: radio Dyer et al. 2009, X-ray (Bamba et al. 2008), GeV (Condon et al. 2017), TeV (Acero et al. 2010); G150.3+4.5: radio (Gerbrandt et al. 2014), X-ray and GeV (Devin et al. 2020); G296.5 + 10.0: radio (Milne & Haynes 1994), GeV (this work), HESS J1534−571: radio (Maxted et al. 2018), GeV (Araya 2017), X-ray and TeV (H.E.S.S. Collaboration et al. 2018b); RCW 103: radio (Dickel et al. 1996), GeV (Xing et al. 2014); G78.2+2.1: radio (Wendker et al. 1991; Zhang et al. 1997; Kothes et al. 2006; Gao et al. 2011), X-ray (Leahy et al. 2013), GeV (Abeysekara et al. 2018) and TeV (Fleischhack 2019); G279.0+1.1: radio (Woermann & Jonas 1988; Duncan et al. 1995), GeV Araya (2020); N132D: radio (Dickel & Milne 1995), X-ray (Hughes et al. 1998; Bamba et al. 2018), GeV (Y. L. Xin et al. 2020, in preparation), and TeV (H.E.S.S. Collaboration et al. 2015).

2022AandA...668A..50S__Ginski_et_al._(2016)_Instance_2

To test IADI, we made a model of a disk in scattered light. This model is 400 × 400 pixels, each pixel having a physical size of 1 × 1 au. Multiple rings are implemented, inspired by observations of disks (e.g., HD 97048 Ginski et al. 2016; RX J1615 de Boer et al. 2016 and TW Hydrae van Boekel et al. 2017), see panel a in Fig. 2. A specific inclination and rotation is achieved using the warpAffine function from OpenCV (Bradski 2000). Young planet-forming disks are still gas rich and dust particles are stratified due to gas pressure along the vertical axis. Therefore, flaring is implemented in the model via an offset of the rings with respect to the center of the ring (see the detailed discussion in de Boer et al. 2016). For our disk model, we are using the power-law profile for the scattering surface height H and the separation r found by Ginski et al. (2016) for the disk around HD 97048. This profile describes the flaring discovered in this source reasonably well up to a separation of ~270 au. Considering that the model disk used will have a separation of 200 au, this formula will be sufficient to simulate the disk height2. Illumination effects of the central star are implemented via a ~1/r2 intensity dependence from the center of the disk, making the inner part of the disk brighter compared to the outer part. Moreover, the intensity also depends on the light scattering angle via the phase function. Because a physical model is beyond the scope of this work, a “pseudo” phase function is implemented to mimic the same asymmetries in light distribution seen in observed disks via I = cos ϕ, where the intensity I depends on the cosine of the azimuthal angle ϕ. This pseudo phase function depends on the azimuthal angle instead of a scattering angle on which a real phase function would depend. This makes the part of the disk facing toward the observer appear brighter than the part facing away in a fairly simple way. Lastly, the model is put through a Gaussian convolution kernel from the scipy ndimage package (Virtanen et al. 2020) to remove sharp edges and give a finite resolution to the model. The final three steps are shown in the top row of Fig. 2.

2021MNRAS.507.6012Z__Kendrick_2018_Instance_1

Being a benchmark system H + H2, H + HD, and their isotopic counterparts have received much attention over the last several decades (Marinero et al. 1984; Zhang & Miller 1989; D’Mello et al. 1991; Harich et al. 2002; Gao et al. 2015; Yuan et al. 2018a, b, 2020). Most early experimental and theoretical investigations were centered around benchmarking theory against experiments and providing improved descriptions of the H3 potential energy surfaces (PES; Boothroyd et al. 1996; Mielke, Garrett & Peterson 2002; Yuan et al. 2018a, b). Among the available PESs for the H3 system, the one by Boothroyd et al. (1996) referred to as the BKMP2 PES and by Mielke et al. (2002) referred to as the CCI PES, nearly equally well account for most experimental data for H + H2, H + HD, and D + HD collisions. These PESs have also been able to account for even subtle effects such as the GP (Kendrick et al. 2015; Hazra et al. 2016; Croft et al. 2017; Kendrick 2018, 2019). The GP effect, while not significant at temperatures relevant to astrophysics, is important below 1 K as illustrated in a series of calculations on H + HD (v, j) collisions for vibrational levels v = 4 − 9 (Kendrick et al. 2015; Hazra et al. 2016; Croft et al. 2017; Kendrick 2018, 2019). Though several prior studies of Flower and co-workers (Flower 1999, 2000; Flower & Roueff 1999; Wrathmall et al. 2007) have reported rate coefficients for H + HD collisions, due to the approximations involved in the scattering calculations (e.g. neglect of hydrogen atom-exchange), the reliability of the available rate coefficients has been a source of debate (Desrousseaux et al. 2018). Recently, Desrousseaux et al. (2018) reported rate coefficients for pure rotational transitions for j ≤ 10 within the v = 0 vibrational level using accurate quantum calculations that include the exchange channel. In this paper, we report rate coefficients for state-to-state rovibrational transitions in HD induced by H atoms between and within the v = 0 and 1 vibrational levels and for temperatures ranging from T = 1–1000 K.

2016MNRAS.461.4406A__Santos_et_al._2015_Instance_1

However, we note that the quoted eccentricity for the inner planet in HD 155358 is 0.17 ± 0.03 (Robertson et al. 2012a). The mean value is exceeded for the standard run at 4000 orbits after going into resonance with the implication that the planets could not have been in resonance longer than this time. If this is the case, the above discussion would have to be modified to allow the planets to migrate independently from larger radii before converging on to resonance close to their final locations. This is likely to need to be considered for different possible exterior disc models and in addition the planets may have built up their masses as they went (see e.g. Tadeu dos Santos et al. 2015). These considerations are beyond the scope of this paper. None the less, because the migration rates for single planets and the resonantly coupled planets are in general similar, the estimated starting radii would also be similar for disc models that are similar to those we considered. But note that the attained eccentricities depend on the eccentricity damping rates which depend on the details of the disc model (see Crida et al. 2008). For example, we found that for the same amount of relative resonant migration, the entirely inactive disc model led to smaller eccentricities while the 3D layered model led to larger eccentricities. Thus, it is important to note that there is uncertainty as to how long the planets could have been in resonance. In the same context, we comment that migration in the completely active disc model was slower by a factor of ∼1.6 compared to the standard case on account of its lower mass, that being determined so as to maintain the same steady-state accretion rate as in the standard case. Furthermore, the potential importance of a residual inner gaseous disc for damping the eccentricity of the inner planet and so preventing the eccentricities of both planets from continuing to increase in the later stages of the orbital evolution has been stressed by Crida et al. (2008). In addition, Murray, Paskowitz & Holman (2002) indicate that a residual disc of planetesimals could produce a similar effect.

2016ApJ...827...75L__Nishizawa_&_Nakamura_2014_Instance_1

According to general relativity, in the limit in which the wavelength of gravitational waves is small compared to the radius of curvature of the background spacetime, the waves propagate with the velocity of light, i.e., c (see Will 1998, and references therein). In other theories, the speed could differ from c. Let us define the parameter 4 If the gravitational wave velocity is subluminal (i.e., ), then cosmic rays lose their energy via gravitational Cerenkov radiation significantly. The detection of ultrahigh-energy cosmic rays thus imposes a stringent constraint (i.e., the “subluminal constraint”), depending on the Galactic or extragalactic origin of such particles (Caves 1980; Moore & Nelson 2001). However, there is no theoretical argument (or pathology) against GWs propagating faster than light (see Nishizawa & Nakamura 2014; Blas et al. 2016, and references therein) and the weak bounds from radiation damping in binary systems are (Yagi et al. 2014). The time lag of arrival times between the GW and the simultaneously radiated photons is 5 where is the differential distance the photons have traveled. Note that in this work we take the flat cosmological model (i.e., ), , and is Hubble’s constant (Riess et al. 2011; Ade et al. 2014). In general, ς may be a function of the GW frequency (f) and especially when graviton mass is non-zero (i.e., ), which gives , where h is Planck’s constant. For simplicity we assume a constant ς and focus on the association of GWs with electromagnetic counterparts at redshifts . Hence Equation (5) yields (see also Will 1998; Nishizawa & Nakamura 2014) 6 In reality the photons and the coalescence are not usually simultaneous and we have , where and are the differences in arrival time and emission time, respectively, of the GW and the photons. In most cases, it is rather hard to get an a priori value for . Assuming (i.e., the GW and the electromagnetic counterparts were emitted simultaneously; see Nishizawa (2016) for a more general discussion), we constrain the absolute amplitude of ς as 7 We call the above process the “canonical approach” to measuring the GW velocity directly, in which the graviton and photon are assumed to make the same journey (i.e., EEP is guaranteed). The advantage is that as long as a GW/GRB association is established one can constrain directly. In Section 4.2 we outline an approach to measure the GW velocity with a simultaneous test of EEP.

2018MNRAS.476.1835F__Danforth_et_al._2010_Instance_1

In the classical unified model, blazars constitute a class of active galactic nuclei (AGNs) viewed at small angles from the jet axis (Blandford & Rees 1978; Antonucci 1993; Urry & Padovani 1995). Traditionally, blazars have been further splitted into two subclasses based on the strength of the features present in their optical spectra. While flat-spectrum radio quasars (FSRQs) show emission lines with equivalent width ≳5 Å, in BL Lacertae objects (BL Lacs) the non-thermal synchrotron radiation of the jet completely dominates the optical/UV emission, ending up in a typical featureless power-law spectrum. This makes the determination of their redshift via the detection of absorption/emission lines from the nuclear emission and/or from the host galaxy particularly challenging, even with 8–10 m class telescopes (e.g. Sbarufatti et al. 2005a, 2006, 2009; Landoni et al. 2013; Sandrinelli et al. 2013; Shaw et al. 2013; Falomo, Pian & Treves 2014; Pita et al. 2014; Paiano et al. 2016; Rosa-Gonzalez et al. 2017 for a review). In past years, several alternatives have been proposed to constrain the redshift of BL Lac objects, including the detection of intervening absorption features either from the halo of lower redshift galaxies (e.g. Shaw et al. 2013; Landoni et al. 2014) or from the neutral hydrogen in the intergalactic medium (e.g. Danforth et al. 2010; Furniss et al. 2013); the spectroscopy of galaxies in the environment where the blazars are embedded (e.g. Muriel et al. 2015; Farina et al. 2016); the detection of molecular emission lines from the host galaxy (e.g. Fumagalli et al. 2012); the study of the effect of the interaction with the extragalactic background light (EBL) in the blazar emission in the GeV and TeV domain (e.g. Prandini et al. 2010; Prandini, Bonnoli & Tavecchio 2012). In particular, the narrow distribution in luminosity of BL Lac host galaxies (Urry et al. 2000; Sbarufatti, Treves & Falomo 2005b) opened the possibility to use them as standard candles, and thus to measure their distance via broad-band imaging (e.g. Nilsson et al. 2008; Meisner & Romani 2010; Kotilainen et al. 2011). The main challenge of this approach is to accurately remove the bright central emission that typically outshine the host galaxy. Given the average 1.0 arcsec (or 3.2 kpc in the considered cosmology) effective radius calculated from the collection of z ≲ 0.6 BL Lac hosts observed with HST by Scarpa et al. (2000), it is clear that images with an exquisite spatial resolution and high contrast are necessary to unveil the faint and diffuse starlight emission around the bright, point-like emission from the active nucleus. In this paper, we exploit the capabilities of the new Advanced Rayleigh guided Ground layer adaptive Optics System (ARGOS; Rabien et al. 2010) mounted on the Large Binocular Telescope (LBT; Hill & Salinari 2004; Hill et al. 2012) to collect high-resolution near-infrared (NIR) LUCI 1 (i.e. LBT Utility Camera in the Infrared; Seifert et al. 2003; Ageorges et al. 2010) observations of HESS J1943+213. This blazar was detected by HESS in the very high energy (VHE) domain (i.e. at E >100 GeV) during a VHE galactic survey (H.E.S.S. Collaboration et al. 2011), making it the only BL Lac object known located in the Galactic plane. Broad Ks-band images gathered with the 3.5 m CAHA telescope revealed the presence of an extended emission that has been attributed to the host galaxy of HESS J1943+21 (Peter et al. 2014). A comparison with the typical size of blazar host derived by Cheung et al. (2003) allowed Peter et al. (2014) to set a lower limit on the redshift of z > 0.03. This is consistent with the z > 0.14 derived from the fit of the spectral energy distribution of HESS J1943+21 (Cerruti 2011; H.E.S.S. Collaboration et al. 2011) and with the z 0.45 limit obtained via modelling the attenuation of the VHE emission by the EBL (Peter et al. 2014). A tighter constraint on the redshift is however necessary to understand the nature of the VHE emission and to derive the EBL properties.

2022AandA...663A..50B__Seibert_et_al._2005_Instance_1

In the absence of dust, the spectral emission of a normal star-forming galaxy is dominated by stellar populations of different ages with superimposed nebular emission, mainly in the form of recombination lines as well as continuum. The interaction with dust has a dramatic effect, both dimming and reddening the emission from stars and ionized gas. This negatively impacts our ability to measure star formation as energetic photons produced by massive young stars are far more easily attenuated than longer wavelength photons, and even a small quantity of dust can lead to a significant attenuation in the ultraviolet (UV). In the case of particularly dust-rich galaxies, it can render their detection in the rest-frame UV especially difficult. However, as the FUV emission vanishes due to dust attenuation, this dust re-emits the absorbed energy in the mid-infrared (MIR) and far-infrared (FIR), which can in turn be exploited to trace star formation. Except for the most extreme cases (e.g., when the dust content is negligible or, conversely, when almost all of the UV photons are absorbed by dust), an attenuation correction must be carried out to retrieve the star formation rate (SFR). One of the most direct ways is to simply apply a hybrid SFR estimator combining the rest-frame UV with the IR (e.g., Hao et al. 2011; Boquien et al. 2016). The obvious downside is that this requires observations of the dust emission that are costly and difficult to obtain, and even more so at increasing redshifts, where they tend to be limited to vanishingly small samples. With the rest-frame UV emission being relatively easy to obtain from the ground from z ∼ 2 and beyond, techniques have been developed to relate the UV slope (β) to the UV attenuation (the IRX-β relation). While this approach initially appeared to work remarkably well in the case of starburst galaxies (Meurer et al. 1999), there is now ample evidence that there is no tight universal relation between the UV slope and the attenuation (e.g., Buat et al. 2005; Seibert et al. 2005; Howell et al. 2010; Casey et al. 2014). In fact, this relation relies on two strong underlying assumptions: the intrinsic UV slope of the stellar populations in the absence of dust and the exact shape of the attenuation curve. Numerous studies have analyzed their respective impact in an attempt to understand why and when such relations fail and build more reliable ones (e.g., Kong et al. 2004; Boquien et al. 2009, 2012; Popping et al. 2017, and many others). In particular, the recent study of Salim & Boquien (2019) found that the diversity of attenuation curves is a strong driver of the scatter around the IRX-β relation. This finding, which is consistent with simulations (Narayanan et al. 2018b; Liang et al. 2021), is especially important in that we can observe a broad variety of attenuation curves at all redshifts (e.g., Salmon et al. 2016; Buat et al. 2018; Salim et al. 2018). With the shape of the attenuation curve being strongly dependent on the relative geometry of stars, ionized gas, and dust (Salim & Narayanan 2020), from the disturbed morphologies observed at higher redshifts, we can only expect important variations there as well (e.g., Faisst et al. 2017). However, due to the great difficulty in measuring them and given the sparsity of the data available, our knowledge of attenuation curves beyond z = 4 remains limited. In effect, most observational studies on the attenuation properties of distant galaxies tend to concentrate on redshifts between 2 and 4 (e.g., Noll et al. 2009b; Buat et al. 2012, 2019; Reddy et al. 2012, 2015; Shivaei et al. 2015; Álvarez-Márquez et al. 2016; Salmon et al. 2016; Fudamoto et al. 2017, 2020b; Lo Faro et al. 2017; Álvarez-Márquez et al. 2019; Reddy et al. 2018; Koprowski et al. 2020). There is only a handful of examples at higher redshift (Capak et al. 2015; Scoville et al. 2015; Bouwens et al. 2016; Barisic et al. 2017; Koprowski et al. 2018). Because of the inherent limits of the observations, studies based on numerical simulations of galaxies at very high redshift (e.g., Mancini et al. 2016; Cullen et al. 2017; Di Mascia et al. 2021) are an important source of information. However, they lead to contrasted results, finding both flat (Cullen et al. 2017) and steep (Mancini et al. 2016) attenuation curves.

2021MNRAS.501.4148L__Hatzes_2014_Instance_1

If the separation between the period of the planet and all the other periodic signals is large enough, and the RV signal has a similar or larger semi-amplitude, it is possible to determine the RV semi-amplitude for a USP planet without any assumptions about the number of planets in the system or the activity of the host star. Under such conditions, during a single night, the influence of any other signal is much smaller than the measurement error and, thus, it can be neglected. If two or more observations are gathered during the same night and they span a large fraction of the orbital phase, the RV semi-amplitude of the USP planet can be precisely measured by just applying nightly offsets to remove all the other signals (e.g. Hatzes et al. 2010; Howard et al. 2013; Pepe et al. 2013; Frustagli et al. 2020 for a recent example). Such an approach, also known as floating chunk offset method (FCO; Hatzes 2014), has proven extremely reliable even in the presence of complex activity signals, as shown by Malavolta et al. (2018). In our case, the shortest, next periodic signal (i. e. TOI-561 c at 10.78 d) is ≃24 times the period of TOI-561 b (i. e. the USP planet at 0.45 d), with similar predicted RV semi-amplitude, making this target suitable for the FCO approach. Thanks to our observational strategy (see Section 2.2), we could use 10 different nights for this analysis. Most notably, during two nights, we managed to gather six observations spanning nearly 5 h, i. e. more than 40 per cent of the orbital period of TOI-561 b, at opposite orbital phases, thus, providing a good coverage in phase of the RV curve. We did not include RV measurements with an associated error greater than 2.5 m s−1 (see Appendix B1). We performed the analysis with PyORBIT as specified in Section 5, assuming a circular orbit for the USP planet and including a RV jitter as a free parameter to take into account possible short-term stellar variability and any underestimation of the error bars. From our analysis, we obtained a RV semi-amplitude of Kp = 1.80 ± 0.38 m s−1, corresponding to a mass of Mp = 1.83 ± 0.39 M⊕. The resulting RV jitter is j 0.9 m s−1(84.13th percentile of the posterior). We show the phase folded RVs of the USP planet in Fig. 9. Since the greater reliability of this method over a full fit of the RV data set is counter-balanced by the smaller number of RVs, we decided not privilege one over the other. Therefore, we assumed as final semi-amplitude and mass of TOI-561 b the weighted mean of the values obtained from the two methods (FCO approach and joint photometric and RV fit), i. e. Kb = 1.56 ± 0.35 m s−1, corresponding to a mass of Mb = 1.59 ± 0.36 M⊕. Table 5 lists the above-mentioned values for TOI-561 b.

2016ApJ...832...57P__Vasquez_&_Markovskii_2012_Instance_1

We employ two types of kinetic codes, hybrid particle-in-cell (PIC) and full PIC simulations. Both types make use of the P3D family of codes (Zeiler et al. 2002), in hybrid PIC (e.g., Parashar et al. 2011) mode, and fully kinetic PIC mode (e.g., Wu et al. 2013b). All simulations discussed here are performed in the 2.5D geometry (two-dimensional (2D) grid and all three components of field vectors). The hybrid simulation has (where is the ion inertial length, with c the speed of light and the proton plasma frequency), , 200 particles per cell, , cold isothermal electrons with . The simulation is initialized with energy only in wavevectors that have . v and b fluctuations are chosen with a specified initial spectral shape, Gaussian random phases, and only in essentially incompressive modes of the system. This simulation was also used in a recent study of variance anisotropy in kinetic plasmas (Parashar et al. 2016). The first full PIC simulation has , , 200 particles per cell, , . The initial condition is the Orszag–Tang vortex (OTV) (e.g., Orszag & Tang 1979; Dahlburg & Picone 1989; Parashar et al. 2009; Vasquez & Markovskii 2012). This simulation was performed for a recent study of transition from kinetic to MHD-like behavior (Parashar et al. 2015). The final PIC simulation (Turb812) has , , 400 particles per cell, , . The initial condition is MHD-like, and more “turbulent,” with v and b fluctuations excited in a band of wave-vectors with with a specified initial spectrum. This simulation was done as part of a recent study that discussed the relation of timescales at the proton gyroscale and their relation to relative proton–electron heating (Matthaeus et al. 2016). PIC codes have an inherent noise associated with them due to the finite number of particles per cell. While performing these simulations, the two most important numerical criteria that we paid attention to were: (i) excellent conservation of total energy (less than a few percent change in any fluctuation energy), and (ii) the particle noise in the spectrum was significant only at scales much smaller than the scales of interest (Debye length for PIC and di for hybrid PIC). On this basis, the modest number of particles employed here was considered adequate. As an additional measure, we employed filtering (e.g., Wan et al. 2012) to remove particle noise at grid scales prior to computing gradients (e.g., vorticity).

2016ApJ...819...51L__Svensson_et_al._2010_Instance_1

Further diagnostics are clearly needed to form firm conclusions. There are likely to be three routes through which this can come. The first is via spectroscopy of the bumps in any further examples. High quality spectroscopy, allied to detailed modeling can yield diagnostics even in the case of relatively weak or featureless spectra, as recently demonstrated in the case of the ultra-long and luminous supernovae pairing GRB 111209A/SN2011kl (Greiner et al. 2015). The unique identification of features expected in luminous SNe (e.g., turn-off due to line blanketing, absorption lines seen in SLSNe) or TDFs (e.g., blueshifted narrow lines from streams (Strubbe & Quataert 2011)) would then provide a clinching argument as to the origin of the bumps in the longest high-energy transients. A second route arises through studying the locations of the transients within their hosts. Swift J1644+57 clearly arises very close to the galactic nucleus, and Swift J2058+0516 is also consistent with the nucleus of a much fainter galaxy (Pasham et al. 2015). In the case of GRBs, approximately 1/6 of examples are consistent with a galaxy nucleus (Fruchter et al. 2006; Svensson et al. 2010); this number may be lower for SLSNe (Lunnan et al. 2015) although the origin of SLSNe in the nuclei of galaxies may be ambiguous (e.g., Dong et al. 2016). Further examples, all in the nuclei of their hosts, would rapidly remove any SNe model from consideration. Finally, we can also consider the host galaxy more globally. TDFs can be observed in quiescent, non-star-forming galaxies while SLSNe are thought to arise from massive star collapse (Gal-Yam 2012) and in principle should occur only in star forming galaxies. A prime model for SLSNe is that they arise from supernovae in which the shock wave is re-energized by the spin-down energy of a recently formed magnetar (Kasen & Bildsten 2010). While magnetars similar to those suggested to power SLSNe can be formed via accretion induced collapse of two merging white dwarfs (Usov 1992; Levan et al. 2006), and may provide a similar energy input, in the case of a white dwarf merger there would be minimal remnant to re-energize, and hence no luminous SNe. This means that the presence of an extremely long event within an quiescent elliptical galaxy would rule out SNe models, and strongly favor an origin as a relativistic tidal flare. Since a reasonable fraction (∼50%) of candidate tidal disruptions arise from passive systems, (i.e., those with little sign of star formation) (e.g., Arcavi et al. 2014) such a test should be possible with only a handful of additional examples since we would expect to observe an example in a system without star formation in the near future.

2017MNRAS.468.2206M__Ceplecha_(1951)_Instance_1

The first identification of a possible meteor shower with a radiant point in the constellation of Ursa minor was by Denning (1912), occurring around the winter solstice. However, very few observations were subsequently made. Hoffmeister recorded its activity in 1914, but its existence was not confirmed until 1945 (Becvr 1946). There are two reasons for the lack of records of the Ursids. First, the Ursids have very low activity levels in most years, its Zenith hourly rate (the number of meteors that would be observed under good observing conditions in one hour if the radiant was at the zenith) is usually ZHR 10 (Jenniskens 1994). Secondly, the weather can be bad in mid-December and many observers choose to only observe the more predictable Geminids. Ceplecha (1951) showed that the annual Ursid shower was related to comet 8P/Tuttle, a Jupiter family comet with a period of 13.6yr (JPL-HORIZONS, ssd.jpl.nasa.gov) and a perihelion distance slightly greater than 1au. This means that the comet is usually brightest (close to perihelion) and close to the Earth at roughly the same time. Hence, it has been observed at all perihelion passages since discovery apart from 1953, when observing conditions were poor throughout. These perihelion passages were in 1858, 1872, 1885, 1899, 1913, 1926, 1940, 1953, 1967, 1980, 1994 and 2008. As it can take a considerable time after being ejected from the nucleus for meteoroids to disperse away from the nucleus locality, an enhancement is generally to be expected in stream activity at the time when the comet is close to perihelion and new meteoroids are injected (see Williams, Johnson Fox 1986 for an early discussion and mathematical formulation of this). An enhancement at some such times has also been observed in the Ursids, where the ZHR reaches around three times the normal rate (e.g. 1900, 1914, 1953, 1981 and 1996, data taken from www.meteorshowersonline.com). However, in the case of the Ursids, several perihelion passage years do not show any significant enhancement and so this is unlikely to be the explanation. Instead, Jenniskens etal. (2007) suggested that they are caused by cometary material released at very old comet perihelion passages between ad300ad1400 and appear as a wide and stretched stream (which we will call and refer to as a filament).

2019ApJ...879...52S__Wei_et_al._2010_Instance_1

The lack of data at complementary wavelengths also makes resolved multiwavelength analyses applied to low-redshift galaxies, such as the Schmidt–Kennicutt relation (the correlation between galaxies’ SFR and gas mass surface densities; e.g., Schmidt 1959; Kennicutt 1989, 1998), significantly less common at high redshift. High-resolution CO observations are critical for evaluating where high-redshift galaxies fall on the true surface density version of the Schmidt–Kennicutt relation, where ΣSFR and Σgas can be compared on a pixel-by-pixel basis within individual galaxies (as done for local galaxies; e.g., Kennicutt et al. 2007; Bigiel et al. 2008, 2011; Wei et al. 2010; Leroy et al. 2013). Many high-redshift analyses use star formation and gas properties averaged over the entire galaxy (e.g., Buat et al. 1989; Kennicutt 1989, 1998; Daddi et al. 2010b; Genzel et al. 2010; Tacconi et al. 2013) or avoid the additional uncertainties in source size and scaling factors by using the total luminosities of the star formation and gas tracers (e.g., Young et al. 1986; Solomon & Sage 1988; Gao & Solomon 2004). These different methods for determining SFRs and gas masses make it difficult to compare studies that focus on different galaxy populations, leading to significant uncertainties in the power-law index of the Schmidt–Kennicutt relation and the relative placement of different galaxy types in the ΣSFR–Σgas plane. Accurately characterizing the Schmidt–Kennicutt relation is important, since offsets imply a difference in star formation efficiency (SFE), and the power-law index probes the underlying physical processes of star formation (for example, a linear correlation would imply supply-limited star formation, whereas superlinear correlations occur if star formation depends on cloud–cloud collisions or total gas freefall collapse times; e.g., Tan 2000; Krumholz & McKee 2005; Ostriker & Shetty 2011). Systematic differences in the Schmidt–Kennicutt relation between different galaxy populations would imply important differences in their star formation processes.

2022AandA...665A..68M__Kempen_et_al._2009_Instance_1

Within a star-forming cloud core, the protostar is the main source of luminosity and heat due to the release of gravitational energy from contraction and material accretion. The amount of protostellar heating dictates the temperature structure of the cloud core. In an idealized spherical scenario, the temperature alone would dictate the snowline locations within the cloud core. However, star-forming cloud cores are not spherically symmetric. The outflow cavity, flattened structures around the protostar (e.g., pseudo-disks and rotationally supported disks; hereafter referred to as disks for simplicity), and variations within the envelope density can all impact how heat is distributed within the cloud core. Studies have shown that heating mainly escapes through the outflow cavity in deeply embedded sources (van Kempen et al. 2009; Yıldız et al. 2015; Murillo et al. 2018a). Thus the extent of chemical richness in the outflow cavity provides insight into the luminosity of the protostar and the physical conditions of the envelope (e.g., Drozdovskaya et al. 2015; Murillo et al. 2018b; Tychoniec et al. 2019, 2020). Observations of embedded protostars (so-called Class 0 and I systems) have shown the presence of disks, both as flattened dust continuum structures (e.g., J∅rgensen et al. 2009; Enoch et al. 2011; Persson et al. 2016; Segura-Cox et al. 2018; Tobin et al. 2020) and rotationally supported disks traced in molecular gas (e.g., Murillo et al. 2013; Harsono et al. 2014; Yen et al. 2015, 2017; Maret et al. 2020). These disks show a wide range of geometries and radii ranging from a few 10 AU up to ~200 AU. Additional studies have shown that the presence of a disk can alter the temperature profile along the equator (disk mid-plane) of the cloud core (e.g., Murillo et al. 2015, 2018b; van’t Hoff et al. 2018b; Hsieh et al. 2019a). Multiplicity, that is two or more protostars within a single cloud core, can produce further asymmetries due to differences in luminosity from the multiple components and their locations with respect to each other (e.g., Chen et al. 2009; Koumpia et al. 2016; Murillo et al. 2016, 2018b). At an early evolutionary stage, protostellar luminosity is dominated by accretion, that is accretion luminosity (Hartmann & Kenyon 1996). Variability in protostellar luminosity has been detected toward several targets (V1647 Ori: Ábrahám et al. 2004; Andrews et al. 2004; Acosta-Pulido et al. 2007; Fedele et al. 2007; Aspin & Reipurth 2009; OO Serpentis: Kóspál et al. 2007; CTF93 216-2 Caratti o Garatti et al. 2011; VSX J205126.1: Covey et al. 2011; Kóspál et al. 2011; HOPS383 Safron et al. 2015; S255IR-SMA1 Safron et al. 2015; Liu et al. 2018; EC53 Herczeg et al. 2017; Yoo et al. 2017). Such variability is considered to be a product of the nonuniform accretion of material with a variable amount and frequency onto the protostar, that is to say episodic accretion (Audard et al. 2014).

2022MNRAS.515.2188H__Rorai_et_al._2018_Instance_3

In this work, we follow the method for measuring the IGM thermal state based on Voigt profile decomposition of the Ly α forest (Schaye et al. 1999; Ricotti et al. 2000; McDonald et al. 2001). In this approach, a transmission spectrum is treated as a superposition of multiple discrete Voigt profiles, with each line described by three parameters: redshift zabs, Doppler broadening b, and neutral hydrogen column density $N_{\rm H\, {\small I}}$. By studying the statistical properties of these parameters, i.e. the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution, one can recover the thermal information encoded in the absorption profiles. The majority of past applications of this method constrained the IGM thermal state by fitting the low-b-$N_{\rm H\, {\small I}}$ cutoff of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution (Schaye et al. 1999, 2000; Ricotti et al. 2000; McDonald et al. 2001; Rudie, Steidel & Pettini 2012; Boera et al. 2014; Bolton et al. 2014; Garzilli, Theuns & Schaye 2015, 2020; Hiss et al. 2018; Rorai et al. 2018). The motivation for this approach is that the Ly α lines are broadened by both thermal motion and non-thermal broadening resulting from combinations of Hubble flow, peculiar velocities, and turbulence. By isolating the narrow lines in the Ly α forest that constitutes the lower cutoff in $b-N_{{{{\rm H\, {\small I}}}}{}}$ distributions, of which the line-of-sight component of non-thermal broadening is expected to be zero, the broadening should be purely thermal, thus allowing one to constrain the IGM thermal state. However, this method has three crucial drawbacks. First, the IGM thermal state actually impacts all the lines besides just the narrowest lines. Therefore, by restricting attention to data in the distribution outskirts, this approach throws away information and reduces the sensitivity to the IGM thermal state significantly (Rorai et al. 2018; Hiss et al. 2019). Secondly, in practice, determining the location of the cutoff is vulnerable to systematic effects, such as contamination from the narrow metal lines (Hiss et al. 2018; Rorai et al. 2018). Lastly, the results from this approach critically depend on the choice of low-b cutoff fitting techniques, where different techniques might result in inconsistent T0 and γ measurements (Hiss et al. 2018; Rorai et al. 2018).

2022AandA...666A..44K__Martig_et_al._2009_Instance_1

Meanwhile, the quiescence of the stellar disks (albeit possibly not entirely passive), is the first indication of the presence of an apparent “outside-in” quenching mode in high-redshift massive galaxy populations, in contradiction to the classic “inside-out” configuration (Lang et al. 2014; Tacchella et al. 2015; Breda & Papaderos 2018). We consider various possible modes of quenching. Since we observe the galaxies to be undergoing an outside-in quenching, this could not have primarily occurred due to feedback from active galactic nuclei (AGN; Alatalo et al. 2015), known to quench galaxies inside-out (Tacchella et al. 2018). We also find no evidence of AGN activity from X-ray observations (Daddi et al. 2021), although a radio excess in Galaxy-C is indicative of weak past AGN activity in that galaxy. The process of morphological quenching (Martig et al. 2009), where the formation of a stellar bulge stabilizes the disk against further star-formation, is also improbable. The galaxies are far from being bulge-dominated, essential for this mode of quenching to be applicable, based on their stellar mass distributions. Also unlikely is cosmological starvation (Feldmann & Mayer 2015) since the availability of gas has already been established in RO-1001. Finally, ram-pressure stripping (RPS; Gunn & Gott 1972) could be regarded as a possible contributor as it is known to remove gas from external regions of galaxies (Bravo-Alfaro et al. 2000; Vollmer et al. 2001; Fumagalli et al. 2009; Boselli et al. 2014; Loni et al. 2021), therefore resulting in an apparent outside-in quenching (for a review, Boselli et al. 2022). It could also lead to compression of the gas in the galaxy which could result in the lopsidedness (although the stellar morphology would not be affected). However, as shown in Appendix B, a conservative lower-limit of the radius up to which RPS can remove gas from the sample in RO-1001 would be 10–15 kpc. This is more than an order of magnitude higher than the kpc-scale cores beyond which the galaxies have suppressed star-formation. Hence we do not expect RPS to be playing a major role.

2021ApJ...921...19W__Arcones_&_Montes_2011_Instance_1

The abundances for cold and hot tracers with different Ye treatments are shown in Figure 12 for model s15_F120_R006. In general, the abundances for cold and hot tracers show a clear iron peak. The cold component is characterized by the odd–even distribution from carbon to calcium, following the progenitor composition. The hot component reaches heavier elements than the cold one with a slight dependency of the abundances on the exact electron fraction. The different assumptions for the initial Ye lead to some variability for the abundances beyond iron covering all expected possibilities for yields from neutrino-driven supernovae. The production of elements in the region of Sr, Y, and Zr is more efficient for slightly neutron-rich conditions, corresponding to RLn = 1.35 (Arcones & Montes 2011; Arcones & Bliss 2014). We observe a very similar behavior and dependency of abundances on the Ye distribution for all exploding models of the 15 M⊙ progenitor. As a summary, we show in Figure 13 the final abundances only for the narrow Ye distribution with RLn = 1 for all models. We do not find any clear correlation between the abundances and the final explosion energy, although we see slight dependency on the explosion energy at ∼1 s after bounce. The lack of correlation is probably due to our simple treatment of the neutrinos and the correction of the electron fraction, as well as to the fact that we are calculating abundances only for the 15 M⊙ progenitor. Simulations with detailed neutrino transport and for several progenitors are necessary to narrow the uncertainties in the abundances and to link those to other astrophysical conditions. Even if our results are not completely conclusive for abundances beyond iron, they indicate that there is not a strong variability for iron group elements. This may be important to estimate uncertainties in the production of 56Ni and 44Ti as shown in Figure 14 where we present an overview of all models including cold and hot components and variations of Ye.

2022MNRAS.513L..78D__Marcos_2020_Instance_1

The fields targeted by our proof-of-concept mini-survey were observed at relatively high airmasses (see Section 2). Therefore, it can be argued that our null result could be due to enhanced extinction caused by observing at such low elevations. However, this is likely not the case because the same EURONEAR collaboration carried out a nearly concurrent mini-survey looking for Atira and Vatira asteroids (see e.g. Greenstreet, Ngo & Gladman 2012) using the same instrumental setup and similar automated reduction pipeline. Atiras or Interior Earth Objects (IEOs) have aphelion distances 0.983 au and can only be observed at low solar elongations (often 70○); Vatiras have aphelia 0.718 au, are observed at very low solar elongations (typically 40○–45○), and just one such object is currently known (Bolin et al. 2020; de la Fuente Marcos & de la Fuente Marcos 2020; Greenstreet 2020), 594913 ‘Ayloi’chaxnim (2020 AV2). Although the nearly concurrent EURONEAR small-scale survey (12 nights with an average time of 1.5 h per night devoted to searching for Vatira and Atira asteroids towards the evening and early morning skies) did not find any new Vatiras or IEOs, it did observe at elevations in the range 15○–30○ and recovered known objects at apparent magnitudes close to or above 23 – for example 2017 HO20, a main belt asteroid that was observed near conjunction. Furthermore, Fig. 2, top panel, shows the r′-magnitude distribution of the detected stars from one representative pointing out of the 77 (×5) pointings intended to recover the TESS candidate in Cepheus. The histogram was produced using the matplotlib library (Hunter 2007) with sets of bins computed using numpy (van der Walt, Colbert & Varoquaux 2011; Harris et al. 2020) by applying the Freedman and Diaconis rule (Freedman & Diaconis 1981). Fig. 2, bottom panel, shows the expected S/N for WHT prime-focus imaging as a function of the exposure time from the exposure time calculator (ETC)11 for a similar camera (Red + 4, PFIP) for observations carried out under a seeing of 0${_{.}^{\prime\prime}}$9 at an airmass of 2.3 in dark time and R = 23.2 mag. TNOs have colour index V − R in the range 0.2–1.2 (see e.g. Peixinho, Delsanti & Doressoundiram 2015). Consistently, we are confident that we reached a limiting magnitude r′= 23.0 mag, which actually corresponds to $V\, \gt $23.0 mag (in fact, it could be 23.2–24.2 mag). Therefore, our distant TNO candidate survey could have detected candidates 9 and 11 in table 2 of Rice & Laughlin (2020) unless both candidates are much fainter than predicted or they are false positives in TESS data. In any case, Rice & Laughlin (2020) stated that many if not most of the high signal significances reported in their table 2 could be the result of unmodelled systematic errors. On the other hand, in the fields observed towards Cepheus, there are a few bright stars that might have hidden a putative moving object with the properties of candidate 9; we estimate that the probability of having missed the candidate as a result of it moving projected towards one of those bright stars is 1 per cent (considering the area affected by the bright stars and associated diffraction spikes).

2017MNRAS.469.4933L__Friedmann_et_al._1996_Instance_1

IC 59 has a lower surface brightness than IC 63, which may be the result of both lower column densities as well as a larger physical distance from the illuminating star (Blouin et al. 1997; Karr, Noriega-Crespo & Martin 2005) compared to IC 63. Because of their rare illumination geometry and proximity to Earth, these two nebulae have been studied intensively at wavelengths ranging from the Lyman limit of hydrogen to the hydrogen line and continuum near 21 cm as ideal test cases for PDR models (Perrin & Sivan 1992; Jansen, van Dishoeck & Black 1994; Blouin et al. 1997; Luhman et al. 1997; Hurwitz 1998; Habart et al. 2004; France et al. 2005; Karr et al. 2005; Thi et al. 2009; Fleming et al. 2010; Miao et al. 2010). As a result of these studies, the physical conditions in these nebulae are exceptionally well constrained. The distances of the nebulae from the illuminating star are about 1 pc, assuming that they are located near the plane of the sky occupied by γ Cas (Friedmann et al. 1996). Thus, the incident radiation field at locations within the two nebulae is essentially unidirectional, with the contribution from γ Cas about 650 times (Habart et al. 2004) more intense than the average ISRF in the solar vicinity at the front PDR in IC 63 facing γ Cas. This leads to well-defined PDRs that are viewed essentially edge-on. This is the case particularly in IC 63. Thus, it is possible to observe radiation from different emission processes requiring photons of different energies for their excitation, such as recombination lines from ionized hydrogen and sulphur, fluorescence from photoexcited molecular hydrogen, emissions from ionized and neutral polycyclic aromatic hydrocarbons (PAHs), and dust-scattered continuum at different depths behind the front of the PDR. In this regard, IC 63 is a more ideal geometry for the morphological study of ERE than NGC 7023, where the illuminating star, HD 200775, is centrally embedded and where projection effects make it more difficult to spatially separate different emission processes (Witt et al. 2006; Berné et al. 2008). One of the prime objectives of this study is to determine the spatial distribution of the ERE in IC 59 and IC 63 in relation to other emissions with known excitation requirements. For this analysis, we take advantage of the wavelength dependence of the opacity of the ISM consisting of atomic and molecular gas and interstellar dust in the UV–optical spectral range (Ryter 1996). An added benefit of the special geometry of IC 59 and IC 63 with respect to γ Cas is that scattering by dust occurring here at large scattering angles near 90° is rather inefficient, which makes it easier to separate other UV/optical emission components from the scattered light, including the ERE. The subsequent chapters of this paper are organized as follows: In Section 2, we present new optical observations of IC 59 and IC 63 and discuss their reductions and absolute intensity calibrations. Additional archival observations obtained from the WISE, Spitzer and Herschel Space Observatories will be introduced as well. In Section 3, we present the analysis of the ERE morphology in the nebulae based on digital image subtraction and division. We will estimate the ERE intensity using the colour-difference technique and determine the wavelength region of ERE excitation. A discussion of the implications for the ERE emission process and specific ERE carrier models will follow in Section 4, with conclusions to be presented in Section 5. Data tables with the results of measurements of the relative surface brightnesses of IC59 and IC63 are presented in the appendices.

2021MNRAS.501.3046F__Thorstensen_et_al._1991_Instance_1

Cataclysmic variables (CVs) are strong interacting binaries. They are systems with an accreting white dwarf (WD, the primary) and a red dwarf (the donor). The donor star is on a late-type main sequence, or sometimes in a slightly evolved state. An accretion disc forms and accreted material reaches the WD in the case in which the magnetic field of the primary is too weak to interrupt the accretion flow (B 0.01 MG): such systems are referred to as non-magnetic CVs, characterized by their eruptive behaviour (Warner 1995). Mass and angular momentum transfer from the donor star to the WD drive a rich variety of variability, as well as the evolution of these systems (Knigge, Baraffe & Patterson 2011). Nova-like variables (NLs) and dwarf novae (DNs) are subtypes of non-magnetic CVs: in addition to the more frequent eruption of DNs, there are many differences between them. NLs have high accretion rates (∼10−8 M⊙ yr−1), so are in a persistent high state, and they gather above the period gap in the period distribution of CVs (Thorstensen et al. 1991); while the accretion rates of DNs are lower than those of NLs by one or two orders of magnitude (≤10−9 M⊙ yr−1), their luminosities only match those of NLs during DN outbursts and their periods are more commonly below the period gap. The possible evolutionary links between the two classes were discussed by Shara et al. (1986) as part of the hibernation scenario, in which systems are NLs immediately after a nova burst and then metamorphose into DNs if their luminosity becomes sufficiently low within several centuries. The hibernation hypothesis also predicts that CVs will experience novae, NLs, and DNs within 104−105 yr and repeat the cycle during their lifetimes. The NL system V1315 Aql was estimated to experience a nova outburst about 500–1200 yr earlier (Sahman et al. 2018): this is consistent with nova-induced cycles in hibernation theory. Kovetz, Prialnik & Shara (1988) estimated that the mass-transfer rate declines at a rate of 0.012 mag yr−1 for the century following outburst. Despite enhanced interest in long-term observations of post-novae and their final decline inspired by the hibernation hypothesis, analysis of the secular evolution of NLs is not yet well known.

2018ApJ...856..136P__Pingel_et_al._2013_Instance_1

Depending on the specific driver, the characteristics of turbulence will then be imprinted within the ISM mainly as three-dimensional density and velocity fluctuations, and these fluctuations have been traditionally studied via correlation functions such as the spatial power spectrum (SPS) (e.g., Crovisier & Dickey 1983), Δ-variance (e.g., Stutzki et al. 1998), and structure function (e.g., Padoan et al. 2002; Burkhart et al. 2015b). In particular, the SPS approach has been applied to observations of various Galactic and extragalactic environments (e.g., Plume et al. 2000; Dickey et al. 2001; Elmegreen et al. 2001; Burkhart et al. 2010; Combes et al. 2012; Zhang et al. 2012; Pingel et al. 2013), showing power spectral slopes β roughly ranging from −2.7 to −3.7 depending on the tracers used (e.g., H i, carbon monoxide (CO), and dust). These slopes essentially provide information on the relative amount of structure as a function of spatial scale and can be compared with theoretical models of turbulence (mainly numerical simulations) to characterize turbulence cascade (e.g., Burkhart et al. 2010), to determine the influence of shocks (e.g., Beresnyak et al. 2005), to reveal the injection and dissipation scales of turbulent energy (e.g., Kowal & Lazarian 2007; Federrath & Klessen 2013; Chen et al. 2015), and to trace the evolution of MCs (e.g., Burkhart et al. 2015a). The proximity and abundance of multi-wavelength observations make MCs in the solar neighborhood an ideal laboratory for probing the impact of turbulence on their formation and evolution. In this paper, we focus on the Perseus MC, which is a nearby (∼300 pc; e.g., Herbig & Jones 1983; Černis 1990), low-mass (∼2 × 104 M⊙; e.g., Sancisi et al. 1974; Lada et al. 2010) cloud. Its star formation activities, as well as atomic and molecular gas content, have been extensively examined over the past decade (e.g., Ridge et al. 2006; Jørgensen et al. 2007; Pineda et al. 2008; Lee et al. 2012, 2014, 2015; Mercimek et al. 2017), revealing that the cloud consists of several individual dark and star-forming regions (e.g., B5, B1, B1E, IC 348, and NGC 1333) and is actively forming low- to intermediate-mass stars (see Bally et al. 2008 for a review).

2022MNRAS.517..130P__Navarro,_Frenk_&_White_1996_Instance_1

The catalogue represents a luminosity-complete sample of galaxies with r-band absolute magnitude Mr ≤ −19 in a (300h−1Mpc)3 comoving volume at z = 0. The mock contains both central and satellite galaxies, populated in dark matter haloes identified in an N-body simulation having 10243 particles with a flat ΛCDM WMAP7 cosmology (Komatsu et al. 2011; Ωm = 0.276, h = 0.7). To achieve the luminosity completeness threshold of Mr ≤ −19, haloes containing ≥40 particles are considered. Since the halo concentration cannot be reliably measured for haloes with fewer than about ∼400 particles, we use the method presented by Ramakrishnan, Paranjape & Sheth (2021) to assign concentrations cvir (assuming Navarro, Frenk & White 1996, NFW profiles) conditioned on the mass and local tidal environment of individual haloes.2 The galaxies were populated using a halo occupation distribution (HOD) model and H i-optical scaling relations calibrated by Paul, Choudhury & Paranjape (2018) and Paul, Pahwa & Paranjape (2019) using luminosity- and colour-dependent clustering measurements from the Sloan Digital Sky Survey (SDSS; York et al. 2000; Zehavi et al. 2011) and H i-dependent clustering measurements from the Arecibo Legacy Fast ALFA survey (ALFALFA; Giovanelli et al. 2005; Guo et al. 2017). Each galaxy in the mock is assigned absolute magnitudes in SDSS u, g, and r bands (with a threshold Mr ≤ −19 imposed by the SDSS clustering measurements) and a stellar mass m* using a colour-dependent mass-to-light ratio. The H i-optical scaling relation additionally leads to a fraction ${\sim}60{{\ \rm per\ cent}}$ of galaxies to be assigned an H i mass $m_{\rm{H}\,{\small I}}$. PCS21 presented extensive tests of this algorithm. We focus in this work on the population of mock central galaxies containing massive H i discs, with $m_{\rm{H}\,{\small I}}\ge 10^{9.7}h^{-2}M_{\odot }$; the resulting ∼50 000 such objects in our catalogue form a volume-complete sample of H i-selected galaxies.

2015ApJ...805..163D__Liang_et_al._2010_Instance_1

In the field of GRBs, evidence of PFD jets has been collected independently in several directions. First, a prominent thermal emission component as expected in the fireball-internal-shock model (e.g., Mészáros & Rees 2000) has been seen only in a small fraction of GRBs (e.g., GRB 090902B, Ryde et al. 2010; Zhang et al. 2011). The majority of GRBs either show no evidence of a thermal component or a weak, sub-dominant thermal component (e.g., Abdo et al. 2009; Guiriec et al. 2011; Axelsson et al. 2012). These GRBs require that the GRB central engine is highly magnetized, and jet is still PFD at the emission site (Zhang & Pe’er 2009; Gao & Zhang 2015). Next, strong linear polarization was discovered during the prompt gamma-ray emission phase for some GRBs (Yonetoku et al. 2011, 2012), and during the reverse-shock-dominated early optical afterglow emission phase for some others (Steele et al. 2009; Mundell et al. 2013), which hint at the existence of globally ordered magnetic fields in the jet. Furthermore, strong PeV neutrino emission as predicted by the MFD models has not been observed from GRBs so far (Abbasi et al. 2012), which is consistent with the expectation of the PFD models (Zhang & Kumar 2013). Finally, the MFD internal shock (IS) model for GRBs also suffers some criticisms, such as low energy dissipation efficiency (Kumar 1999; Panaitescu et al. 1999), electron fast cooling (Ghisellini et al. 2000), the electron number excess (Bykov & Mészáros 1996; Daigne & Mochkovitch 1998; Shen & Zhang 2009), and inconsistency with some empirical (Amati/Yonetoku) relations (Zhang & Mészáros 2002; Liang et al. 2010). Zhang & Yan (2011) proposed a novel PFD outflow model named as “the Internal-Collision-induced MAgnetic Reconnection and Turbulence (ICMART),” which can potentially keep the merits of the IS model but alleviate the criticisms faced by the IS model mentioned above. The main idea of the ICMART model is that the GRB jets are PFD. The Poynting flux is catastrophically discharged at a relatively large distance (e.g., 1015 cm) from the central engine through collision-induced magnetic reconnection. The magnetic energy is converted to particle energy and radiation efficiently, leading to a very high radiation efficiency as demanded by the GRB data (Panaitescu & Kumar 2002; Zhang et al. 2007). A PFD jet has less leptons than the MFD model so that the electron excess problem is avoided. A large emission radius favors a moderately fast cooling, which can account for the right low-energy spectral index observed in GRBs (Uhm & Zhang 2014). It also gives a natural explanation of the seconds-duration of “slow variability component” observed in GRBs (Gao et al. 2012). The rapid “fast variability component” can be interpreted within this scenario as mini jets due to locally Lorentz boosted regions (see also Lyutikov & Blandford 2003; Narayan & Kumar 2009)3 3 Lyutikov & Blandford (2003) and Narayan & Kumar (2009) proposed that GRB variability is a consequence of mini jets due to relativistic outflow from reconnection or relativitic turbulence. There is no simple explanation to the observed slow variability component in these models. Zhang & Yan (2011) attributed the two variability components (slow and fast) as due to central engine activity and mini jets, respectively. Monte Carlo simulations by Zhang & Zhang (2014) showed that the ICMART model can indeed reproduce the observed GRB light curves. . It is speculated that turbulent reconnection in a moderately high-σ flow can give rise to relativistic motion of mini jets within the bulk relativistic motion of the jets.

2021AandA...655A..72S___2019_Instance_3

In this paper, we report on spectroscopic CH3CN, CH3OH (methanol), and dust continuum observations with the Atacama Large Millimeter/submillimeter Array (ALMA) at 349 GHz with an angular resolution of 0′′.1. We exploit the CH3CN (19K–18K) K-ladder, with excitation energies ranging from 168 K (for K = 0) to 881 K (for K = 10), to probe, at different radii, the physical conditions in the accretion disk of an early-type young star. We targeted the star-forming region G023.01−00.41, at a trigonometric distance of 4.59 $^{+0.38}_{-0.33}$ −0.33 +0.38 kpc from the Sun (Brunthaler et al. 2009), where we recently revealed the accretion disk around a young star of 104.6 L⊙, corresponding to a ZAMS star of 20 M⊙ (Sanna et al. 2019, their Fig. 1); the disk was imaged by means of spectroscopic ALMA observations of both CH3CN and CH3OH lines at 0′′.2 resolution inthe 230 GHz band. The disk extends up to radii of 3000 au from the central star where it warps above the midplane; here, we resolve the outer disk regions in two apparent spirals projected onto the plane of the sky. We showed that molecular gas is falling in and slowly rotating with sub-Keplerian velocities down to radii of 500 au from the central star, where we measured a mass infall rate of 6 × 10−4 M⊙ yr−1 (Sanna et al. 2019, their Fig. 5). The disk and star system drives a radio continuum jet and a molecular outflow aligned along a position angle of 57°, measured east of north (Sanna et al. 2016, their Fig. 2); their projected axis is oriented perpendicular to the disk midplane whose inclination with respect to the line-of-sight was estimated to be less than 30° (namely, the disk is seen approximately edge-on; Sanna et al. 2014, 2019). Previously, we also measured the average gas conditions over the same extent of the whole disk, by means of Submillimeter Array (SMA) observations of the CH3CN (12K–11K) emission, and we estimated a kinetic temperature of 195 K and CH3CN column density of 5.1 × 1016 cm−2 (Sanna et al. 2014, their Fig. 2 and Table 4).

2018MNRAS.477.3520L__Abolfathi_et_al._2018_Instance_3

Over time, the data releases have treated the Balmer line regions in different ways. The presence of the artificial curvature was first reported by Busca et al. (2013) in the context of the DR9 data release. To minimize this effect, a different scheme was used in DR12 (Alam et al. 2015, see their table 2) by using a linear function (instead of an iterative b-spline procedure) to interpolate the flux over the masked regions. Surprisingly, we observe that this data reduction change was only applied to the Balmer β, γ, and δ lines but not applied to the Balmer α line. For the latter one, the original iterative spline interpolation scheme has been used all along. As a result, an absorption-like feature at the location of Balmer α is found in SDSS data releases 9 up to now, i.e. the latest data release 14 (Abolfathi et al. 2018). To illustrate this, we show examples of calibration vectors for SDSS BOSS DR9 (Ahn et al. 2012; Dawson et al. 2013), DR12 (Alam et al. 2015), eBOSS DR14 (Dawson et al. 2016; Abolfathi et al. 2018) data release as well as calibration vectors for the MaNGA survey (Bundy et al. 2015) DR14 data release (Abolfathi et al. 2018) in Fig. 6. In the upper panel, we show small-scale features in calibration vectors from the same plate (number 3647) processed by the DR9 (blue), DR12 (green), and DR14 (red) pipelines. The spectra are obtained by normalizing the large-scale features in calibration vectors with a cubic b-spline with break points separated by 50 Å. The three spectra are mostly identical while the smooth curving features at the wavelengths of Balmer series in DR9 disappear in DR12 and DR14. From DR12, the pipeline corrects the features with linear interpolation across wavelength regions with Balmer β, γ, and δ lines (Alam et al. 2015). One can also observe the change in the median residual spectra of DR9 and DR12 in Fig. 1. However, the Hα feature remains uncorrected from DR9 to DR14. We also show a DR7 calibration vector (black) in which most of the wiggles are absent. As pointed previously, this is due to the fact that the DR7 pipeline interpolates the calibration vectors using an effective scale larger than that used in subsequent data releases.

2020AandA...640L..11B__Segretain_1996_Instance_2

Another possibly important cooling delay may arise from the phase separation of 22Ne during crystallization (Isern et al. 1991; Althaus et al. 2010). Our current best understanding is that at the small 22Ne concentrations typical of C/O white dwarfs (∼1% by number), the presence of 22Ne should not affect the phase diagram, except near the azeotropic point of the C/O/Ne phase diagram. Thus, the crystallization of the C/O core initially proceeds as in the case without 22Ne with no redistribution of neon ions between the solid and liquid phases. After a significant fraction of the core has crystallized, the temperature approaches the azeotropic point and the existing calculations indicate that the liquid phase is enriched in 22Ne relative to the solid (Segretain 1996; García-Berro et al. 2008). The 22Ne-poor solid is lighter than the surrounding liquid and floats upward where it eventually melts. This gradually displaces the 22Ne-rich liquid downward toward the solid–liquid interface until the azeotropic composition is reached, thereby releasing a considerable amount of gravitational energy. Given our very limited knowledge of the ternary C/O/Ne phase diagram (Segretain 1996; Hughto et al. 2012), this effect cannot be quantitatively implemented in our evolution models. However, we note that our current understanding of 22Ne phase separation is remarkably consistent with the missing cooling delay. In Fig. 2 we show the luminosity function obtained by adding an artificial 0.6 Gyr delay when 60% of the core is crystallized. These parameters are entirely consistent with those found in preliminary studies (Segretain 1996; García-Berro et al. 2008) and yield an excellent fit to the crystallization pile-up3. Based on the current (albeit limited) knowledge of the C/O/Ne phase diagram, we propose that the phase separation of 22Ne in the advanced stage of crystallization significantly contributes to the pile-up in the luminosity function of 0.9−1.1 M⊙ white dwarfs (Fig. 2).

2016MNRAS.462.1508G__Gaur_et_al._2012c_Instance_1

The new sample of HSPs gave us an opportunity to see the optical IDVs of HSPs and compare its properties with optical IDVs of LSPs. We started a dedicated project to search for optical IDV in HSPs and after doing 62 nights of IDV observations of HSPs which gave us 144 LCs (41 in B band, 62 in R band, and 41 in B−R colour) of five HSPs (Mrk 421, 1ES 1426+428, 1ES 1553+113, 1ES 1959+650, and 1ES 2344+514). Interestingly, we found that, four HSPs did not show any IDV (Gaur, Gupta & Wiita 2012a; Gaur et al. 2012b,c), but only one HSP 1ES 1426+428 for which we have the least observations have shown IDV in six LCs out of eight LCs (Gaur et al. 2012c). Our this pilot project gave us 6 IDV LCs out of 144 LCs searched for IDV i.e ∼4 per cent LCs have shown IDV. We explained it by density inhomogeneities and bends in the bases of the jets by Kelvin–Helmholtz instabilities (Romero, Cellone & Combi 1999). We gave an alternative explanation i.e. since in HSPs, the optical band lies below the SED peak, hence, we should see changes in the efficiency of acceleration of, and/or in the rates at which energy is radiated by, the highest energy electrons available for synchrotron emission would have a more retarded effect on optical variability in HSPs (Gaur et al. 2012b). In LSPs, the optical band is dominated by highest energy electrons emitting synchrotron radiation and probably the X-ray emission is dominated by the comparatively lower energy electrons emitting the inverse Compton radiation, hence their X-ray variability is less pronounced than optical variability. If SED peak is really responsible for IDV properties, then we suspected that X-ray IDV LCs in LSPs should not show any IDV at all or show on rare occasions. With this motivation, here we present the X-ray IDV study of almost complete sample of 10 LSPs and 2 ISPs observed by XMM–Newton since its launch and we found that the LSPs show very less IDV 2 out of 50 LC i.e. 4 per cent in X-ray bands. We have reported above the similar finding for HSPs in optical bands.

2018AandA...616L...2K__Frew_et_al._(2016)_Instance_2

The distances to planetary nebulae (PNe) have always faced the difficulty that nearby targets were lacking that could be reached well by direct methods. Trigonometric parallaxes have been obtained in a homogeneous long time-line campaign by the US Naval Observatory (USNO; Harris et al. 2007) and from the Hubble Space Telescope (HST; Benedict et al. 2009). Other studies (Acker et al. 1998; Smith 2015) showed that Hipparcos spacecraft parallaxes do not seem to be reliable. It was assumed that contamination by the emission of the surrounding nebulae caused these problems. Another model-independent method for distances to PNe are a cluster membership, as studied extensively by Majaess et al. (2007, 2014), and as discussed in Frew et al. (2016). In addition to these model-independent methods, a wide variety of statistical, model-dependent individual distance scales have been derived. The most frequently used of these are certainly those that are based on surface brightness versus angular sizes. They sometimes include optical depth corrections. All these methods have to be calibrated against a data set of nebulae with known distances. The older, widely used method is based on the 6 cm radio continuum flux, either using the ionized mass concept of Daub (1982) in the calibrations of Cahn et al. (1992) and Stanghellini et al. (2008), or by means of the radio continuum brightness temperature as used by van de Steene & Zijlstra (1994) and calibrated with a Galactic bulge sample. The newest model developed by Frew et al. (2016) is based on similar ideas, but makes use of the optical Hα surface brightness and a wide set of various calibrators. Moreover, they use a completely homogeneous data set for the brightness data derived earlier by themselves (Frew et al. 2013). Smith (2015) and Frew et al. (2016) described the underlying physics and assumptions for all these methods in detail. With the upcoming Gaia project (Gaia Collaboration 2016), a new era was expected to start for many classes of objects. The first step into this was described by Stanghellini et al. (2017) based on the combined Tycho + Gaia DR1 solution called TGAS (Michalik et al. 2015). With the second Data Release of Gaia (hereafter GDR2; Gaia Collaboration 2018), a complete homogeneous data set based only on Gaia measurements is available now for the first time. We present here the comparison of this new data set with common previous calibrations of PNe distances. Moreover, we compare it to the preliminary TGAS results in Stanghellini et al. (2017). Finally, we discuss possible caveats using the current GDR2.

2020MNRAS.498.1801K__Hosler,_Jensen_&_Goldshlak_1957_Instance_1

Water ice is ubiquitous in the cold regions of the Universe, owing to the fact that hydrogen and oxygen are the two most abundant elements to form a solid such as icy dust particles and comets. It is, therefore, commonly accepted that the essential component of dust particles and planetesimals in protoplanetary discs is water ice beyond the so-called snow line, at which the temperature of gas is low enough for water vapour to condense into ices (e.g. Cyr, Sears & Lunine 1998). Reactive accretion of water ice from hydrogen and oxygen atoms on the surface of dust particles takes place in the dense core of molecular clouds where the growth of dust particles has been observed by scattering of stellar radiation (Steinacker et al. 2010). It is worthwhile noting that laboratory experiments on the coagulation growth of water-ice particles have a long history outside astronomy and planetary science, since coagulation is observed in daily life and is a plausible route to the formation of snowflakes (e.g. Faraday 1860; Hosler, Jensen & Goldshlak 1957). Recent works on laboratory measurements of cohesion between crystalline water-ice particles at vacuum conditions provided encouraging results that dust particles composed of water ice might be much more cohesive than previously believed (Gundlach et al. 2011; Gundlach & Blum 2015; Jongmanns et al. 2017). Form a theoretical point of view, Chokshi, Tielens & Hollenbach (1993) demonstrated that the JKR theory of elastic contact formulated by Johnson, Kendall & Roberts (1971) is a powerful tool for better understanding of dust coagulation. Numerical simulations incorporating the JKR theory have shown that dust aggregates consisting of submicrometre-sized water-ice particles proceed with coagulation growth even at a collision velocity of 50 m s−1 (Wada et al. 2009, 2013). As a result, the majority of recent studies on dust coagulation and planetesimal formation assume that silicate aggregates are disrupted by mutual collision at a velocity of vdisrupt ∼ 1 m s−1, but icy aggregates at vdisrupt ∼ 10 m s−1 (e.g. Birnstiel, Dullemond & Brauer 2010; Vericel & Gonzalez 2019). Such a trendy assumption led Drążkowska & Alibert (2017) to propose planetesimal formation by the ‘traffic jam’ effect at the snow line, provided that sticky water-ice particles grow faster and thus drift toward the central star faster than less-sticky bare silicate particles, implying that aggregates of the former catch up the latter at the snow line, which results in a traffic jam. However, we argue that the importance of water ice to dust coagulation is still open to debate, since water ice is not necessarily stickier than other materials such as silicates and complex organic matter (Kimura et al. 2015, 2020a; Musiolik & Wurm 2019).

2017ApJ...835...79S__Wilson_et_al._1974_Instance_1

The PN central white dwarf generates a significant ultraviolet (UV) radiation field during its transition from the proto-PN stage to the PN stage, typically ∼105 times that of the general interstellar medium (ISM), but decreasing to 10–100 as the remnant envelope expands and the luminosity of the white dwarf declines (Cox 1997). The copious amount of UV photons penetrating the nebula is predicted to photodissociate the remnant molecular material from the AGB stage within a thousand years (e.g., Redman et al. 2003). Indeed, the first molecular searches of PNe turned up negative (e.g., Penzias et al. 1971; Wilson et al. 1974). Subsequently, both CO and H2 have been detected in numerous PNe (e.g., Huggins & Healy 1989; Huggins et al. 1996, 2005; Hora et al. 1999; Likkel et al. 2006). More recent studies have identified other molecules in planetary nebulae with ever increasing complexity. Toward the young PNe NGC 7027 and NGC 6537, for example, HCN, HNC, CCH, CS, SO, H2CO, HCO+, and N2H+ have been identified (Zhang et al. 2008; Edwards & Ziurys 2013). In more evolved nebulae, species such as HCN, HNC, HCO+, CS, and CN have been found (e.g., Cox et al. 1992; Bachiller et al. 1997; Edwards et al. 2014). The very old Helix Nebula (age ∼12,000 years.) has an assortment of interesting molecules, including HCO+, CN, HCN, H2CO, CCH, c-C3H2, and HNC (Bachiller et al. 1997; Tenenbaum et al. 2009), and mapping data have shown that the molecular material is distributed throughout the PN (Zack & Ziurys 2013; Zeigler et al. 2013). SiO, SO, and SO2 have recently been found in M2-48 (age ∼5000 years.) as well (Edwards & Ziurys 2014). Perhaps even more striking is the identification of C60 in several PNe with low central star temperatures (Cami et al. 2010; García-Hernández et al. 2010, 2011, 2012; Otsuka et al. 2013). The presence of long-lived molecular material is believed to arise from shielding by high-density clumps (Howe et al. 1994), as seen in images of the Helix and the Ring (e.g., Meaburn et al. 1992; Speck et al. 2003; Meixner et al. 2005).

2019MNRAS.485.4343C__its_2018_Instance_1

As of 2018, three multimeasurement catalogues including a substantial amount of redshift-independent extragalactic distance measurements have been released: HyperLEDA (Makarov et al. 2014), NED-D (Mazzarella & Team 2007; Steer et al. 2017), and Cosmicflows-3 (Tully, Courtois & Sorce 2016). HyperLEDA includes a homogenized catalogue for extragalactic distances in the nearby universe, with 12 866 distance measurements for 518 galaxies to date. NED-D is the NASA/IPAC Extragalactic Distance catalogue of redshift-independent distances, which compiles 326 850 distance measurements for 183 062 galaxies in its 2018 version. Here, ∼1800 galaxies (∼1 per cent) have more than 13 distance measurements, and 180 galaxies (∼0.1 per cent) have distance measurements using more than six different methods. Cosmicflows-3 is the most up-to-date catalogue, which reports distance measurements for 10 616 galaxies (all of which include errors) using up to four distance determination methods, calibrated with supernova luminosities. However, unlike HyperLEDA or NED-D, Cosmicflows-3 only reports the latest distance measurement for each method. In HyperLEDA, NED-D, and Cosmicflows-3 errors are reported as one standard deviation from the reported distance modulus. Treatment of errors for combining distance moduli across methods or across measurements is suggested by Mazzarella & Team (2007) and Tully et al. (2016) to be based on weighted estimates such as the uncertainty of the weighted mean, albeit with caution partly due to the heterogeneous origin of the compiled data and partly due to Malmquist bias. In the case of NED-D, this is additionally complicated by the fact that many errors are not reported or are reported as zero. In fact, the TF relation method has the largest number of galaxies with non-reported distance modulus errors (884 to date). Even though extragalactic distances measured using the TF relation were originally reported to have a relative error in distance modulus of 10–20 per cent (Tully & Fisher 1977), we consider that this conservative estimate can be improved upon by using a predictive model based on the distance error of galaxies that use the same distance determination method. This requires a robust estimation of the variance of extragalactic distances based on the available data.

2019MNRAS.486.5239D__Galli_et_al._2014_Instance_1

The emission from both types of sources contaminates measurements of the Cosmic Microwave Background (CMB), which contains information on cosmological parameters. The measurement of the CMB polarization, particularly the E-mode damping tail, can help break some of the degeneracies between cosmological parameters. Measurements of the polarization damping tail are expected to become foreground-limited at a smaller angular scale (higher ℓ) than the temperature damping tail, because of the expected low polarization of dusty point sources (see below). Further, the higher contrast of the acoustic features in EE power spectrum compared to astrophysical foregrounds (Calabrese et al. 2014; Galli et al. 2014) will ultimately provide independent and tighter constraints on the standard cosmological parameters, such as the scalar spectral index ns, than those from the temperature data alone. High-resolution measurements of the E-mode polarization will improve the delensing of the primordial B-modes (Seljak & Hirata 2004), ultimately tightening the constraint on the tensor-to-scalar ratio r. As measurements of the small angular-scale fluctuations in the CMB are attaining higher sensitivity and finer resolution, ongoing and planned ground-based CMB surveys, such as Advanced ACTPol (Henderson et al. 2016), SPT-3G (Benson et al. 2014), Simons Observatory (Ade et al. 2019 (SO)), CCAT-prime (Stacey et al. 2018), and CMB Stage 4 (Abazajian et al. 2016; Abitbol et al. 2017) will be capable of extracting information from the E-mode damping tail out to ℓ ≈ 9000. However, the contribution of the extragalactic point sources to the CMB power spectrum increases towards smaller angular scales, and it is expected to be a significant fraction of the CMB polarization power. For example, extragalactic foreground sources are expected to be the predominant contaminant for angular scales smaller than 30′ (ℓ≳ 400) in the 70–100 GHz frequency range (Toffolatti et al. 1998). Hence, characterization of these sources in terms of their spectral and spatial distributions is essential for separating foregrounds from the CMB.

2022MNRAS.511.3477E__Carter_&_Erdélyi_2008_Instance_1

It has been cleared out in the observations that a coronal flux tube could consist of a twisted magnetic field (e.g. Kwon & Chae 2008; Aschwanden et al. 2012; Thalmann et al. 2014; Wang et al. 2015). In a magnetically twisted flux tube, the background magnetic field has an azimuthal component which causes the magnetic field lines to wind around the axis of the tube. The winding number (the number of twist turns around the tube axis) has been reported to have values in the range (0.11, 0.87) (Kwon & Chae 2008). The role of twisted magnetic field in the MHD waves has been studied in many theoretical works (e.g. Bennett et al. 1999; Erdélyi & Carter 2006; Erdélyi & Fedun 2006, 2007, 2010; Ruderman 2007, 2015; Carter & Erdélyi 2008; Karami & Barin 2009; Karami & Bahari 2010, 2012; Terradas & Goossens 2012; Ruderman & Terradas 2015; Ebrahimi & Karami 2016; Ebrahimi et al. 2017; Terradas et al. 2018). Sakurai et al. (1991a) studied resonant absorption of MHD waves in presence of a twisted magnetic field and obtained jumps of the perturbations around the resonance point (see also Sakurai et al. 1991b; Goossens et al. 1992). Ebrahimi et al. (2017) investigated resonant absorption of kink MHD waves and phase mixing of the perturbations in twisted magnetic flux tubes. They showed that depending on the profile of the azimuthal component of the magnetic field and the azimuthal mode number of the global kink wave (m = ±1) magnetic twist can enhance or suppress the decay rate of the kink wave and the rate of phase mixing of the perturbations. Ebrahimi et al. (2017) concluded that even a small amount of twist can have a large impact on the generation of small spatial scales and transferring the wave energy from the global mode to the local Alfvénic oscillations. However, Ebrahimi et al. (2017) neglected the dissipation effects in their analysis. It is known that dissipation could affect the generation of small scales due to phase mixing (McLaughlin et al. 2011; Ebrahimi et al. 2020; Howson et al. 2020).

2021MNRAS.506.5015H__Weinberg,_Miller_&_Lamb_2001_Instance_1

Based purely on quality of fits of atmosphere model spectra to observed spectra obtained here, one composition is not preferred over another for the three older CCOs. The primary differences in fit results when the atmosphere is composed of carbon instead of hydrogen are a ∼40 per cent lower temperature and ∼3 times larger emission radius (to maintain nearly constant $R_{\rm em}^2T^4$). The larger Rem could be an argument in favour of carbon because it is closer to the neutron star radius R, which would imply that the entire surface is essentially at a single temperature and would explain non-detection thus far of pulsations from each of these three CCOs. However, a hot region with radius 3–4 times smaller than R can still produce a pulsed fraction below current limits of 20–40 per cent in the spin period range 0.1–0.4 s of known CCOs (see Section 1; for pulsed fraction dependence on spot size, see, e.g. Psaltis, Özel & DeDeo 2000; DeDeo, Psaltis & Narayan 2001; Weinberg, Miller & Lamb 2001; Bogdanov, Grindlay & Rybicki 2008; Lamb et al. 2009; Bauböck, Psaltis & Özel 2015; Elshamouty et al. 2016). For example, Gotthelf, Perna & Halpern (2010) find that a model which includes a hotspot with Rem/R ∼ 0.4 (and a second smaller spot) is able to produce a pulsed fraction that matches the 11 per cent measured for the CCO in Puppis A. Also of note is the stronger limit on the Cassiopeia A CCO pulsed fraction of 12 per cent for P > 0.01s compared to the other CCOs (see Table 1). Meanwhile, a hydrogen atmosphere is a natural consequence of even a very low-level of accretion from the interstellar medium on to a relatively cool neutron star surface several hundred years after neutron star formation (see Section 5.1 for further discussion). Thus from an evolution standpoint, a hydrogen atmosphere for the older CCOs studied here might be preferred. Future measurements of pulsations or improvements to pulsation constraints could provide stronger indications of their atmosphere composition.

2017MNRAS.469.2720G__Hernández-García_et_al._2015_Instance_1

With all this in mind, our last question is: What powers soft X-rays and [O III] in LINERs? This has no clear answer, but both are not tracing the same mechanism, since none of them match in morphologies. This is a clear difference between type-2 Seyferts and LINERs. In favour of the soft X-ray emission being originated by AGN photoionization, the RGS spectra studied by Gonzalez-Martin et al. (2010b) showed that in at least 30 per cent of their sample, a contribution of photoionization by the AGNs is required due to the presence of radiative recombination continua (RRC) from CV emission line. However, this does not guarantee a dominance of this emission mechanism. Moreover, cone-like morphologies at soft X-rays in some objects in this study point out again to the photoionization by the AGNs being responsible for the soft X-ray emission. Nevertheless, this assumes that we are seing LINERs with an LOS perpendicular to the accretion disc, which might not be the case. Indeed, the ultraviolet and X-ray variability detected for many of these LINERs (Maoz et al. 2005; Hernández-García et al. 2015) is in favour of a direct view of the AGNs (i.e. perpendicular to the disc under the UM). This is consistent with the fact that most of the [O III] morphologies found for LINERs are spheroids, if we assume that the [O III] traces the NLR. In addition, the fact that we detected a clear correspondence between soft X-ray and [O III] morphologies only in objects with log (LHX)>40, and also that all the objects where soft X-rays and [O III] match their morphologies seem to better follow the previously found relation between the size of the region and the hard X-ray luminosity (see Fig. 2 and Section 5.2), may argue in favour of the scenario in which the AGNs do not have enough thrust to ionize in the low-luminosity regime (Elitzur & Shlosman 2006; Elitzur & Ho 2009), ruling out photoionization by the AGNs at both soft X-ray and [O III] emissions. In this case, the most reasonable explanation for the [O III] is the host galaxy emission, which, anyhow, could also be on top of the AGNs, preventing its detection and erasing the connection (González-Martín et al. 2014). The host galaxy can contribute either as star formation or shocks to the total [O III] emission. Regarding the soft X-ray origin, Mingo et al. (2014) confirmed that jets are the main responsible for soft X-ray emission from their sources. In our sample, jets are identified in NGC 1052 (Kadler et al. 2004), where the jet position angle would be consistent with the extended soft X-ray emission shown here.

2020AandA...642A.140G__Guilera_et_al._(2019)_Instance_1

In addition, we also perform two new simulations changing the type I migration recipes from those of Tanaka et al. (2002) to those of Paardekooper et al. (2011) and Jiménez & Masset (2017). With this, we want to analyze whether or not the planet migration trap could be a robust result even using more sophisticated type I migration recipes, which were derived for nonisothermal disks. We remark that despite our simplifications in the computation of the thermodynamics of the disk, we compute the time evolution of the radial profiles of all the quantities needed to calculate the migration recipes mentioned above, that is, we compute the time evolution of the radial profiles of the density, pressure, opacity, and so on while the temperature radial profile remains fixed in time. Explicitly, the simplification lies in the fact that the evolution of the temperature is not linked self-consistently with the evolution of the gas surface density. We can see in Fig. 8 that the density and pressure maximum also acts as a planet migration trap using the type I migration recipes from Jiménez & Masset (2017). However, using the type I migration prescriptions from Paardekooper et al. (2011), the migration trap is broken close to the mass needed for the planet to open a gap in the disk. This happens because now the normalized torque is not only a function of the local gradient of the gas surface density (as in the isothermal case) but also of the local gradient of the temperature, the viscosity, and the mass of the planet (these last two dependencies through the corotation torque). Thus, the combination of such quantities breaks the migration trap at some moment. Guilera et al. (2019) showed that the main differences between the migration recipes from Jiménez & Masset (2017) and Paardekooper et al. (2011) lie in the computation on the corotation torque. Thus, in this case, when the planet reaches a mass of a several tens of Earth masses, the migration trap is broken. At this moment, the planet quickly migrates inwards (due to the fact that it has a mass of ~ 80 M⊕) until it opens a gap at ~1.15 au. We note that the time evolution of the mass of the core and the mass of the envelope for the simulations using the type I migration recipes from Paardekooper et al. (2011) and Jiménez & Masset (2017) are very similar to those of the fiducial simulation. In Fig. 9, we emphasize this by plotting the planet formation tracks as a function of time. Simulations also stopped when the planets opened a gap in the disk. This happens when the total mass of the planet is about 110 M⊕ for the case where we used the migration recipes from Paardekooper et al. (2011) and about 95 M⊕ for the case where we adopt the migration recipes from Jiménez & Masset (2017). The differences in the total mass at which the planet opens the gap are due to the fact that this mass depends on the viscosity of the disk. Inside the ice line, the viscosity is larger (because of the larger α-parameter), and therefore the total mass of the planet has to be higher to open a gap. For the second case, the planet opens the gap in the α transition region between 10−5 (outside the ice line) and 10−3 (inside the ice line). We note that if the thermodynamics of the disk are computed self-consistently, the migration trap can break at a different planet mass depending mainly on the viscosity and thermal diffusivity of the disk (see Morbidelli 2020).

2022AandARv..30....6M__Ellison_et_al._2015_Instance_1

In particular, two main scenarios have emerged: the first one attributes differences in radio morphology to the large-scale (i.e., host galaxy and environment, see also below in this Section) properties of radio-AGN which influence the interaction between the radio jet and the external medium (e.g., Kaiser and Best 2007; Wing and Blanton 2011; Miraghaei and Best 2017; Mingo et al. 2019, 2022), with those between HERGs and LERGs being instead dictated by different fuelling mechanisms as discussed earlier. According to this framework, HERGs are powered by accretion of cold gas, provided by e.g., a recent merger with a gas-rich galaxy, while LERGs accrete hot intergalactic gas from dense environments at a low rate (e.g., Best and Heckman 2012), with fuelling from major mergers strongly disfavoured by recent observations (e.g., Ellison et al. 2015). On the other hand, other works do not observe any difference in the host and/or environmental properties of FRI and FRII galaxies, except in the rare cases of FRII HERGs (e.g., Lin et al. 2010; Capetti et al. 2017b; Jimenez-Gallardo et al. 2019; Massaro et al. 2019, 2020; Vardoulaki et al. 2021) or even between those of HERGs and LERGs (e.g., Fernandes et al. 2015), so that an alternative mechanism for their large-scale radio behaviour has to be invoked. In this case, ageing processes can be thought as the main driver for the observed morphological differences, with an evolutionary pattern that proceeds from FRII HERGs that switch from efficient to inefficient accretion due to gas starvation and transform themselves into FRII LERGs, sources that still maintain their large radio structures thanks to the past nuclear activity at high efficiency (e.g., Ghisellini and Celotti 2001; Tadhunter 2016; Macconi et al. 2020; Grandi et al. 2021). The switch off/change in accretion mode will eventually show in the radio morphology with the delay needed to reach Kpc-to-Mpc distances, and the source will ultimately turn into an FRI galaxy.

2020MNRAS.494.1045B__Raymond_et_al._2009_Instance_1

In our simulations, we assumed that the growth of dust grains and planetesimals occurred during the first stages of planetary accretion. Thus, we used a bimodal disc composed of embryos (60 per cent of the disc’s mass) and planetesimals (40 per cent of the disc’s mass; Izidoro et al. 2015). It was assumed that embryos are formed by oligarchic growth and are thus spaced randomly by 5–10 mutual Hill radii (Kokubo & Ida 1998, 2000) with a density of 3 g cm−3. The mass of each planetesimals is ≈ 0.002 M⊕. In the numerical integrations, the planetesimals do not have gravitational interactions with themselves but only with stars, planets, and protoplanetary embryos. The protoplanetary embryos masses scale as M ≈ r3(2 − x)/2Δ3/2 (Kokubo & Ida 2002; Raymond, Quinn & Lunine 2005; Raymond et al. 2009; Izidoro et al. 2015), where Δ is the mutual Hill radii separations between embryos orbits. As we are using distinct systems with different parameters, the number of embryos and planetesimals are not the same among the systems; see Table 4. Fig. 5 shows the initial conditions of x = 1.5 and 2.5, for all our simulations. Discs with x = 2.5 have embryos more massives in the inner region of the disc, and with 1.5 in the outer region. We chose these two values of the parameter x, besides being used frequently in some works (Raymond, Quinn & Lunine 2004; Raymond et al. 2005; Izidoro et al. 2014a,2015; Izidoro, Morbidelli & Raymond 2014b; Izidoro & Raymond 2018), to study the dynamic evolution in discs that have mass growing as a function of the orbital radius of the bodies and in cases where the mass decreases as a function of the orbital radius (see Fig. 5). This is an important point because we have some systems where the giant planet is in the inner region of the disc and others in the outer region. So we needed to consider the two cases for all systems. The orbital inclination of the embryos and planetesimals is chosen randomly between $10^{-4}\, ^{\circ }$ and $10^{-3}\, ^{\circ }$ with respect to the binary plane, and the eccentricity is chosen between 0 and 0.01.

2017MNRAS.469S.731L__Kolokolova_&_Kimura_2010_Instance_1

The specific radiance received from the optically thin dust plume can be expressed as (1) \begin{equation} L_\lambda =f_{\rm plume}\frac{p}{\pi } \frac{\phi (\alpha )}{\phi (0)} \frac{f_{\lambda }}{r_{\rm h}{^2}} \end{equation} where p is the geometric albedo of the dust particles at wavelength λ, ϕ(α) is the phase function at phase angle α, fλ is the solar irradiance (in W m−2 nm−1 at 1 au), rh is the heliocentric distance in au and fplume is the dust filling factor (e.g. the fraction of a pixel covered by dust). Assuming that the geometric albedo of the dust particles is similar to that of the nucleus, the geometric albedo and specific solar flux are 0.068 and 1.513 W −2 nm−1 for the orange (648.6 nm) filter, and 0.027 and 0.23 W −2 nm−1 for the UV (270.7nm) filter (Fornasier et al. 2015), respectively. The flux ratio ϕ(α)/ϕ(0) can be derived by using the dust phase function (Kolokolova & Kimura 2010). By integrating the radiance over the image area (e.g. within the boxes in Fig. 7) containing the dust from a difference outburst plume, the total cross-section from difference images of the dust jet from within a given time interval can be determined. Then, we used a power-law index g = 3.7 for the size distribution with a constant bulk density of 1000 kg m−3 for all ejected particles. The ejected mass in size interval of a1 a a2 (i.e. 1 μm to 1 mm) is given by (2) \begin{equation} M=\frac{({4}/{3})\pi \rho N}{4-g} \left(a_2^{4-g}-a_1^{4-g}\right), \end{equation} where N is the total number of dust particles in the size interval 1 μm to 1 mm. The estimated dust cross-section and ejected mass are given in Table 2. The uncertainty of the ejected mass is typically of the order of 5–10 per cent, depending on the uncertainty on the brightness integration of the selected box. The averaged mass ejection rate for the outbursts can be estimated from the ejected mass (M) by dividing by the time interval between the difference images. Note that our calculation is based on the chosen time intervals and hence produces lower limits on the mass ejection rates. For example, the estimation of the ejected mass for a major outbursts on July 29 is about 4550 kg. Given a time interval of 18 min, the average mass ejection rate would be of the order of 4 kg s−1. However, if this outburst lasted for a time interval of 5 min only, which is the cadence for the outburst sequence, the corresponding mass ejection rate would be as much as 15 kg s−1.

2016MNRAS.455..552B__Riess_et_al._1998_Instance_1

The plethora of cosmological observations has turned cosmology into a quantitative science. From combining several probes that observe the Universe at different epochs and have different systematics and statistics, emerged the ‘concordance model’ of cosmology, a six parameter model, most of them measured to the accuracy of a per cent. Among these probes, Type Ia supernovae (SNe) are a powerful cosmological tool to directly measure the expansion history of the Universe (see e.g. a recent review of Weinberg et al. 2013). The type Ia SNe are known to be standard candles and one can measure the luminosity distances to them accurately. Several SNe surveys have set strong constraints on cosmological models from the distance–redshift relation (e.g. Riess et al. 1998; Perlmutter et al. 1999; Riess et al. 2007; Sullivan et al. 2011; Campbell et al. 2013). However the measured apparent magnitudes, have a residual scatter arising from its intrinsic scatter and effects due to line-of-sight (LOS) structures. The intrinsic scatter (∼0.4 mag) can be significantly reduced to ∼0.1 mag by empirically calibrating the luminosity curves. The scatter due to photon deflection along the LOS is composed of many different physical effects. The dominant effects are peculiar velocities at z 0.1 and gravitational lensing at z ≳ 0.3. Within the realm of cosmological perturbation theory and the stochastic nature of the LOS structures, all effects are expressed as an integral over the power spectrum with appropriate kinematical factors (Ben-Dayan et al. 2012). The lensing effects create residuals from the best-fitting curve in the magnitude–redshift relation. The lensing magnifications of SNe can be extracted by correlating the residuals with the surface densities of nearby foreground galaxies. The lensing dispersion is roughly proportional to the SNe redshift z, (e.g. Holz & Linder 2005; Ben-Dayan et al. 2013), and specifically for the concordance model, the predicted value is ∼0.06z mag.

2015MNRAS.451.2123T__King_&_Begelman_1999_Instance_1

The results of Ivanova et al. (2003) are somewhat closer to ours. A remaining key difference is that Ivanova et al. (2003) find that their calculated mass-transfer rates for systems with initial orbital periods, Porb, i 0.4 d sometime exceed a critical mass-transfer rate, $\dot{M}_{\rm crit}$ related to the location of the so-called trapping radius (e.g. Begelman 1979; Chevalier 1993; MacLeod & Ramirez-Ruiz 2015b). In systems with very large super-Eddington mass-transfer rates, matter presumably piles up around the NS and forms a growing, bloated cloud engulfing a large fraction of the accretion disc. A system will only avoid a CE if it manages to evaporate the bulk of the transferred matter via the liberated accretion energy at a distance from the NS larger than the trapping radius. Otherwise, the incoming material has too much negative binding energy to be ejected. At the same time, this trapping radius must be located inside the Roche lobe of the NS in order to avoid a CE (King & Begelman 1999). The exact location of the trapping radius, and thus the value of $\dot{M}_{\rm crit}$, is difficult to calculate because it also depends on the cooling processes of the infalling gas (Narayan & Yi 1995; Blandford & Begelman 1999). Nevertheless, in all of our models presented in Table 1 with Porb, i > 0.06 d we find that $|\dot{M}_{\rm He}^{\rm max}|\le \dot{M}_{\rm crit}$, where $\dot{M}_{\rm crit}$ is calculated from equation 15 in Ivanova et al. (2003), and varies from about 3.7 × 10−3 M⊙ yr−1 (for Porb, i = 2.0 d) to 4.3 × 10−4 M⊙ yr−1 (for Porb, i = 0.08 d). Only for Porb, i = 0.06 d, we find a couple of systems where $|\dot{M}_{\rm He}^{\rm max}|> \dot{M}_{\rm crit}$. However, these systems are anyway found to result in a runaway mass transfer and thus formation of a CE. Therefore, our calculated mass-transfer rates from the helium stars donors must be slightly smaller than those calculated by Ivanova et al. (2003). The reason for this, besides from using different stellar evolution codes, is perhaps that our mass-transfer rates are calculated using the prescription by Ritter (1988) whereas Ivanova et al. (2003) adopted the prescription by Tout & Eggleton (1988). To summarize, we caution that all numerical calculations of Case BB RLO could potentially be affected by uncertain accretion processes at high mass-transfer rates exceeding the Eddington limit by ∼4 orders of magnitude.

2018MNRAS.478.2541F__Smith_&_Tombleson_2015_Instance_1

Utilizing the full range of peak absolute magnitudes observed in LBV stars [M ≃  (−13)–(−9) mag; e.g. Smith et al. 2011b] provides a range of peak apparent magnitudes of m ≃  16.5–20.5 mag, for a distance of D ≃ 8 Mpc (Table 1). Hence, if the transient source (m ≈ 21.0 mag; Section 2.1.2) is an LBV star, it must have been observed a short time after its peak (Fig. 2; left-hand column; rows 4–5); quiescent LBV stars can have absolute magnitudes as low as M ≃ −6 mag (e.g. Smith et al. 2011b), or m ≃ 23.5 mag (for a distance of D ≃ 8 Mpc; Table 1). An LBV star provides an adequate explanation for the transient time-scale, the isolation (e.g. Smith & Tombleson 2015; Smith 2016), the lack of a host H ii region (Fig. 2 and 3; see below, however), and the transient/main source offset (d ≈ 0.3 kpc, for D ≃ 8 Mpc; Table 1; see below, however). In addition, (net) fading of the LBV peak event by Δm ≲ 3 mag (over a period of approximately 40 yr) may possibly produce an apparent brightness centroid shift/morphology variation in the main source (Sections 2.1.2 and 2.1.3). However, this scenario has its caveats. First, it is difficult to interpret the flux variability of the main source (Sections 2.1.4 and 2.3.1) within such a context. Secondly, although a fraction of LBV stars are isolated (e.g. Smith & Tombleson 2015; Smith 2016), in the only XMP with a documented LBV star, the LBV star appears embedded within an H ii region (DDO 68; Pustilnik et al. 2017); no clear H ii region at the location of the transient source is discernible in the HST images (Fig. 3; top), although it should be detectable given the typical lifetime of an H ii region (few Myr; e.g. Alvarez, Bromm & Shapiro 2006). Thirdly, a (net) magnitude variation of Δm ≲3 mag of the transient over a period of approximately 60 yr would result in a quiescent LBV source (m ≲ 23.5 mag) that should have been detectable in the HST images (Fig. 3; top). Lastly, as LBV stars brighten, they become redder (e.g. Sterken 2003), which appears to contradict the POSS data (Table 2; Fig. 2; rows 2–5). Consequently, as the LBV scenario explains some of the observables challenged by other scenarios (e.g. transient timeline), it remains a contender for the observed phenomenon.

2020MNRAS.494.5270Z__Pesnell,_Thompson_&_Chamberlin_2012_Instance_1

In particular, the presence of slow magneto-acoustic-gravity waves (MAG waves, hereafter) guided by the magnetic field is supported by increasingly observational evidence in different atmospheric structures, e.g. photospheric flux tubes (Roberts & Ulmschneider 1997), sunspots (Jess et al. 2013; Freij et al. 2014; Khomenko & Collados 2015; Madsen, Tian & DeLuca 2015), coronal loops (King et al. 2003), and coronal plumes (Nakariakov 2006). These oscillations are also used to estimate the formation heights of different emission spectral lines. As a result of the analysis of 3 min oscillations detected in observations from the Atmospheric Imaging Assembly instrument (AIA; Lemen et al. 2012) on-board the Solar Dynamic Observatory (SDO; Pesnell, Thompson & Chamberlin 2012), Deres & Anfinogentov (2015) found that the formation heights of the corresponding spectral lines are consistent with models of the sunspots umbra involving strong temperature gradients of the type used in this paper (e.g. Fontenla et al. 2009). Meanwhile the lower atmospheric magnetic structure is frequently believed to be mostly formed by small magnetic flux tubes of circular cross-section emerging from the photosphere and expanding upwardly in the corona (Solanki, Inhester & Schüssler 2006). The photosphere and chromosphere are known to be dominated by acoustic-gravity waves with observed short periods of ∼[3–5] min. Whereas in the lower corona a wider range of periods are present (up to about 80 min – e.g. Sakurai et al. 2002–), short periods ([3–5] min) have also been detected in certain coronal structures, e.g. coronal loops and intense magnetic flux tubes (Srivastava & Dwivedi 2010; Jess et al. 2012; Reznikova et al. 2012). A rich range of periods are found in sunspots, the periods becoming larger as the distance from the sunspot umbra increases; a likely result of the influence of the magnetic field (Bogdan & Judge 2006; Jess et al. 2013; Yuan et al. 2014; Madsen et al. 2015).

2021AandA...647A.144A__Bretherton_1966_Instance_2

Understanding how inertial waves interact with co-rotation resonances is thus a key issue in quantifying tidal dissipation, especially since waves may deeply interact with the background flow at this particular location, which in turn may alter the background flow (as was proposed first by Eliassen & Palm 1961, for terrestrial mountain waves). In binary systems and for late-type stars, Goldreich & Nicholson (1989) showed that the angular momentum transported by gravity waves and exchanged at co-rotation can lead to the successive synchronisation of the layers, from the base to the top of the radiative envelope. More generally, a body of work in various domains, from astrophysical disks (e.g. Goldreich & Tremaine 1979; Baruteau & Masset 2008; Latter & Balbus 2009; Tsang & Lai 2009) to geophysical fluid dynamics (e.g. Bretherton 1966; Yamanaka & Tanaka 1984), has tried to understand the properties of wave propagation and dissipation around co-rotation and, more generally, at all special locations in fluids that correspond to singularities in the linear wave propagation equation. We will refer to them as ‘critical levels’ in the following (Maslowe 1986), or as ‘critical layers’ in the case of a viscous medium. This distinction is analogous to the distinction between shear layers and attractors of characteristics that are kinds of singularities for the governing equation of inertial waves in a spherical shell. The aforementioned singularities can act very differently, with either severe absorption at the critical level (as in Booker & Bretherton 1967, for stratified vertical shear flows) or no attenuation if the wave propagates in a peculiar direction (Jones 1967; Acheson 1972; Grimshaw 1975a, for stratifiedvertical shear flows with rotation and magnetism). In other cases, a critical level may even give rise to wave amplification under certain conditions related to the first and second derivatives of the mean flow velocity (Lindzen & Tung 1978; Lindzen & Barker 1985, for barotropic and stratified shear flows, respectively). These studies all used an invariant quantity (the Reynolds stress or thewave action for rotating or magnetic flows) as a diagnostic tool to interpret the role of the critical level in terms of energy transmission and to quantify exchanges between the wave and the mean flow (Eliassen & Palm 1961; Bretherton 1966).

2018AandA...618A.145O__Codella_et_al._2016_Instance_1

The chemical composition of protostellar envelopes and their properties along the evolutionary stage of protostars is an important topic in astrochemistry. Since the pioneering work by Cazaux et al. (2003) and Sakai et al. (2008), systematic chemical studies of solar-type protostars (see Ceccarelli et al. 2007; Caselli & Ceccarelli 2012 for a review; also Lefloch et al. 2018) have identified two classes of objects. The first class corresponds to the so-called “hot corinos”, that is, sources which display a rich content in complex organic molecules (COMs) in the central inner regions of the protostellar envelope (see Ceccarelli et al. 2007 for a review; also Taquet et al. 2015). Only a few hot corinos have been identified so far either with single dish or interferometric observations: IRAS16293-2422 (Cazaux et al. 2003; Bottinelli et al. 2004b; Jørgensen et al. 2011, 2016), IRAS2, IRAS4B (Bottinelli et al. 2007), IRAS4A (Bottinelli et al. 2004a; Taquet et al. 2015), HH212 (Codella et al. 2016), L483 (Oya et al. 2017), B335 (Imai et al. 2016), SVS13A (Bianchi et al. 2017), Serpens SMM1, and SMM4 (Öberg et al. 2011). We note that very few sources were investigated in a systematic manner meaning that the COM budget in hot corino sources is very inhomogeneous, making a general picture difficult to come by. Hot corinos share some similarities with the hot cores observed around high-mass stars but they are not scaled-down versions of these. Bottinelli et al. (2007) showed that the abundances of O-bearing species scaled to methanol are higher than those measured in hot cores by one to two orders of magnitude or more. The second chemical class of protostars corresponds to the so-called Warm Carbon Chain Chemistry (WCCC) sources, which have a rich content in C-chains but are poor in COMs. A recent survey of a sample of 36 Class 0/I protostars of the Perseus molecular cloud complex by Higuchi et al. (2018) shows that the majority of the sources observed have intermediate characters between these two distinct chemistry types.

2022AandA...664A.117D__Peters_et_al._2015_Instance_1

We mentioned that our light curves are characterized by two large gaps of one year and seven months plus eight months, as shown in Table 1. From the table, it is apparent that mean and median observed baseline values, computed for the sources in the main sample for each season, are very close to the maximum observed baseline (when not exactly coincident with it) for the corresponding season. Similarly, the mean and median number of visits for each season are very close to the total number of visits (when not exactly coincident with it) for the corresponding season. This shows how, for individual seasons, our dataset can take advantage of a dense sampling, which plays a key role in the context of AGN detection efficiency, as shown in De Cicco et al. (2019). Nevertheless, the two gaps affect the shape of our SF with inadequate sampling in correspondence with some timescales. Sparse and/or irregular sampling is a very common issue in SF analysis (e.g., de Vries et al. 2003; Peters et al. 2015; Simm et al. 2016; Sartori et al. 2019). Indeed, there are works from the literature where the cadence is low but the sampling is regular: as an example, Hawkins (2002) uses quasar light curves from a long-term monitoring program with 24 yearly observations per source to investigate the origin of the emission mechanism in AGN. Nonetheless, one of the advantages in the use of the SF is its relative insensitivity to irregular sampling when sources are considered as an ensemble rather than individually (e.g., Hawkins 2007; Kozłowski 2016; Sartori et al. 2019). As mentioned in Sect. 3.2, Bauer et al. (2009) analyze the effect of irregular sampling by means of simulations, and conclude that the turnover that is observed in the light curve is an effect of sparse sampling at longer timescales, and not a real feature in the SF of AGN. Emmanoulopoulos et al. (2010) also resort to simulations in order to assess whether and to what extent the SF is robust against the presence of gaps in the light curves. They simulated a single light curve and then investigated the effect of three different gaps in the data, representing three different situations: almost periodic data gaps, dense and sparse sampling, and purely sparsely sampled data, corresponding to 57%, 83%, and 92% of the data being removed from a single simulated light curve that is 2000 time units long, respectively. They then used bootstrapping to extract 1000 light curves from each of the obtained light curves with gaps, then compared the results obtained with and without gaps. While they find that the presence of gaps is responsible for the presence of wiggles and bends in the SF, from the right panels of their Fig. 12 we can infer that these wiggles and bends do not alter the slope of the SF obtained from the light curve with no gaps. In this work we are not investigating the turnover as our baseline is not long enough (Sect. 3.2); our analysis is instead focused on the linear region of the SF (and the possible dependence on physical quantities of interest), where the irregularity of the sampling does not constitute a major issue.

2019AandA...631A.106S__Bordé_&_Traub_2006_Instance_1

With a priori knowledge of speckle evolution lifetime (Milli et al. 2016), more evolved a posteriori algorithms may well calibrate the speckle pattern. However, any such method can directly benefit from an active technique that minimizes the static or quasi-static speckles in each science image during an observation. Active suppression of these speckles requires measurement of the electric field associated with the speckles directly from a coronagraphic image using a focal plane wavefront sensor (FPWFS). Several FPWFSs have been proposed such as phase diversity (Bordé & Traub 2006; Give’on et al. 2007; Sauvage et al. 2012) and the self-coherent camera (SCC; Baudoz et al. 2006, 2012). Once the electric field is measured, one or several deformable mirrors (DM) can then be used to minimize the speckle intensity in a region of the image called the dark hole (Malbet et al. 1995). Very encouraging laboratory results have been obtained on the coronagraphic testbeds simulating the space-related environment. The stellar speckle intensity is shown to be reduced by a factor of up to 105 (Belikov et al. 2007; Trauger et al. 2011, 2012; Mazoyer et al. 2013, 2014) which would enable the detection of planets 1010 times fainter than their host star. Several attempts on ground-based Extreme-AO instruments have been performed with moderate results. The stellar speckle intensity has been suppressed by only a factor of up to ten (Savransky et al. 2012; Martinache et al. 2014; Bottom et al. 2017; Matthews et al. 2017; Wilby et al. 2017; Vigan et al. 2019; Galicher et al. 2019) reaching contrast levels of roughly 10−6. Most of these techniques temporally modulate the speckle intensity to measure their phase and at least three images are needed for each estimation of the electric field. The quasi-static speckles that evolve faster than every four images cannot be correctly estimated and therefore set a limitation for the speckle estimation. Addressing this concern, our team proposed the SCC that spatially modulates the speckle intensity so that the associated complex electric field can be measured in every science image. The drawback is that a finer sampling of the coronagraphic image is required as compared to the temporal modulation techniques. The SCC has been developed and rigorously tested in space-related environments on the THD2 bench at the Paris Observatory (Baudoz et al. 2018).

2015MNRAS.447.3243M__Middleton_et_al._2011b_Instance_1

Ultraluminous X-ray sources (ULXs) have been widely observed in the local Universe, with inferred isotropic luminosities above 1039 erg s−1 (Roberts 2007; Feng & Soria 2011). Those below ∼3 × 1039 erg s−1 can be readily associated with accretion on to stellar mass black holes (BHs) (∼10 M⊙) accreting close to or at their Eddington limit (see Sutton, Roberts & Middleton 2013, and references therein). There is now strong evidence to support this assertion, with the discovery of extremely bright ballistic jets from a ULX in M31 (Middleton et al. 2013; Middleton, Miller-Jones & Fender 2014b), which unambiguously links the flow with Eddington rate accretion (Fender, Belloni & Gallo 2004), and the first dynamical mass measurement of the compact object in a ULX, from M101 ULX-1 (Liu et al. 2013). Observations of such ‘low-luminosity’ ULXs (Middleton et al. 2011b, 2012; Kaur et al. 2012; Soria et al. 2012) have revealed changes in the disc emission that may imply the creation of a radiation pressure supported, larger scaleheight flow in the inner regions (Middleton et al. 2012) or magnetic pressure support (Straub, Done & Middleton 2013). Although emission below ∼2 keV is generally heavily photoelectrically absorbed by material in the Galactic plane (e.g. Zimmermann et al. 2001), similar spectral behaviour may also be seen in a small number of Galactic BH X-ray binaries (BHBs) at high rates of accretion (e.g. Ueda, Yamaoka & Remillard 2009; Uttley & Klein-Wolt, in preparation). Such ‘extreme’ high state BHBs probably dominate the ULX population (Walton et al. 2011), yet a significant number of ULXs can still be found at higher luminosities. Those above 1041 erg s−1 are dubbed hyperluminous X-ray sources (HLXs; Gao et al. 2003) and provide the best evidence (Farrell et al. 2009; Davis et al. 2011; Servillat et al. 2011; Webb et al. 2012) for a population of intermediate-mass BHs (IMBHs; Colbert & Mushotzky 1999). Such IMBHs (with masses above those expected from direct stellar collapse: >100s of M⊙) could potentially be formed in globular clusters (Miller & Hamilton 2002, but see Maccarone et al. 2007), through capturing and tidally stripping a dwarf galaxy (King & Dehnen 2005) or mergers in young super star clusters (Portegies-Zwart, McMillan & Gerhard 2003; Portegies-Zwart et al. 2004).

2018ApJ...856...19N__Isern_et_al._1991_Instance_1

Models of the ECSN progenitor cores suggest the onset of the electron-capture instability occurs at a unique ONeMg core mass in the mass range of 1.366–1.377 M⊙. (Miyaji et al. 1980; Nomoto 1984, 1987; Podsiadlowski et al. 2005; Takahashi et al. 2013). Electron captures cause the core to contract, and O and Ne burning is ignited in the central regions and propagates outwards in a deflagration front (Schwab et al. 2015), processing material to nuclear statistical equilibrium, where further electron captures and photdissociation accelerates the collapse (Miyaji et al. 1980; Nomoto 1987; Takahashi et al. 2013). Whether the core collapses or the deflagration disrupts the core depends sensitively on the ignition density (Isern et al. 1991; Jones et al. 2016). If the core does collapse, the explosion proceeds via delayed explosion on short timescales (Mayle & Wilson 1988; Kitaura et al. 2006; Fischer et al. 2010), and 2D simulations suggest the explosion occurs before significant convection has had time to develop (Wanajo et al. 2011) and hence a symmetric explosion results. This, coupled with the steep density gradient at the core surface, leads to very little mass loss from the core; estimates of mass loss include of order 10−3 M⊙ (Podsiadlowski et al. 2005), 10−2 M⊙ (Kitaura et al. 2006), and 1.39 × 10−2 M⊙ (1.14 × 10−2 M⊙) for the 1D (2D) models of Wanajo et al. (2009, 2011). Therefore the ONeMg progenitor core mass is a good estimate of the baryon mass MB of the resulting NS (Podsiadlowski et al. 2005). Indeed, PSR J0737-3039A and the companion to PSR J1756-2251 have gravitational masses consistent with baryon masses ∼1.37 M⊙ when their gravitational binding energies are taken into account (Lattimer & Yahil 1989). Population synthesis calculations incorporating the various binary evolution channels that might lead to production of NSs via ECSNe show that J0737-3039B most likely formed in an ECSN, and the companion to PSR J1756-2251 is consistent with such a formation scenario (Andrews et al. 2015). Other systems with candidates for ECSNe formation also exist (Keith et al. 2009; Chen et al. 2011).

2019MNRAS.482..560M__Kong_et_al._2004_Instance_1

An empirical expression for FUV extinction based on UV reddening of a diverse, UV-selected sample of 200 galaxies (Seibert et al. 2005) is (10) \begin{eqnarray*} A_{\rm FUV}(\beta)=3.978(m_{\rm FUV}-m_{\rm NUV})+0.143, \end{eqnarray*} where mFUV and mNUV are the respective GALEX AB magnitudes.13 The equation is similar to relations derived by others (e.g. Hao et al. 2011). Fig. 9 shows the SFRS galaxies in the IRX–(mFUV − mNUV) (or equivalently IRX–β) space. There is a correlation between AFUV(β) and AFUV(IRX) with Pearson correlation coefficient r = 0.71 and mean 〈AFUV(β) − AFUV(IRX)〉 = 0.33 mag, but the rms scatter in AFUV(IRX) as derived from AFUV(β) is 0.44 dex. Galaxies with $A_{\rm FUV}(\beta)\lesssim 2$ can have bolometric extinctions as high as 6 mag, and AFUV(β) applied to LFUV greatly underestimates their FIR luminosity and therefore SFR. This is consistent with other results (e.g. Kong et al. 2004; Johnson et al. 2006, 2007), which have shown that galaxies having higher current SFR relative to their past averaged SFR are likely to deviate above the IRX–β relation, i.e. have larger AFUV(IRX) for a given AFUV(β). Despite this qualitative agreement, the Kong et al. mean numerical relation for their UV-selected sample of 50 local starbursts is not a good fit to the FIR-selected SFRS data as shown in Fig. 9. Regardless of numerical values, all these studies agree that galaxies with higher SFR are more obscured at fixed β (also see Iglesias-Páramo et al. 2004; Cortese et al. 2006; Moore et al. 2010). At the low SFR end, galaxies with L60 109.3 L⊙, which at $z$ ≈ 0 are mostly early-type cluster galaxies, form two groups. Around 75 per cent of them are near the mean IRX–β relation, but the rest show AFUV(IRX) ≪ AFUV(β). One possibility is that these galaxies have older stellar populations with intrinsically high values of β. In the middle range 109.3 L60 1010.7 L⊙, there is a general trend for AFUV(IRX) to follow AFUV(β) but with rms scatter ∼0.34 dex. At L60 > 1010.7 L⊙, the scatter is ∼0.56 dex.

2020ApJ...888..118M__Soto_et_al._2013_Instance_1

To determine foreground and background contamination by star-forming galaxies, AGNs, shock emission, and extended PAH emission, as well as field stars, we performed the infrared color selection method described in Gutermuth et al. (2009). In Phase I of this method, we used only GLIMPSE sources that have photometry in all four IRAC bands, and have photometric uncertainties σ 0.2 mag in all four bands, which corresponds to a total of 2723 sources. In Figure 4 we show CCDs from the first step of the Phase I selection method, that allowed us to identify contamination from star-forming galaxies and AGNs. Then, we proceeded to the elimination of shock emission and extended PAH emission contamination, as well as YSO class selection as shown on Figure 5. This procedure resulted in a total of 2702 Spitzer/GLIMPSE sources without contamination. We obtained a total of 42 sources classified as Class I, and 177 sources classified as Class II. We continued with a process similar to the Phase II selection method described by Gutermuth et al. (2009), which we slightly modified due to the use of VISTA/VVV data instead of 2MASS data used in the Gutermuth et al. (2009) paper. In this step, both Spitzer/GLIMPSE and VISTA/VVV data were used. Specifically, the Phase II selection method is applied to Spitzer/GLIMPSE sources that lack [5.8] and/or [8.0] detections. The Spitzer/GLIMPSE sources were first matched with their VISTA/VVV counterparts. We only selected high-quality VISTA/VVV detections with σ 0.1 mag, and Spitzer/GLIMPSE detections with photometric uncertainties σ 0.2 mag in the detected bands for this analysis. A cross-match of both catalogs was done using a search radius of 1″, to create a matched list of 13,309 sources. A further selection on these sources was done using their VISTA/VVV magnitudes. We excluded stars with saturated photometry (Soto et al. 2013), by limiting the magnitudes of the detected VISTA/VVV sources to 13.8, 12.8, and 12.8, for the J, H, and KS bands, respectively. A total of 9974 non-saturated sources were selected. Whenever possible we checked for contamination using similar color–color criteria as described for Phase I in Gutermuth et al. (2009), and ended up with a total of 9844 sources without contamination. We selected this sample of 9844 stars where objects with infrared excesses are expected to be found. To select these infrared excess sources, we used the dereddened color selection criteria detailed in Gutermuth et al. (2009) with [K–[3.6]]0 and [[3.6]–[4.5]]0 colors. Displaying the position of all the new infrared excess candidates showed that they are uniformly distributed in the region, thus we consider that our selected data set might still suffer contamination by foreground sources. To eliminate any possible chance of selecting any contaminant, we therefore applied additional selection criteria to these infrared excess sources candidates, as described in Winston et al. (2011) and Megeath et al. (2012), by plotting two CCDs, as shown in Figure 6. The first selection was done using the CCD J–H versus H–[4.5], with the slope of the reddening band (Chen et al. 2013). The second color selection was done using the CCD H–KS versus KS–[4.5] with the slope of the reddening band (Chen et al. 2013). We defined as YSOs all sources passing the selection criteria of the first or of the second CCD, and found a total of 1566 YSOs. Initial inspection of this selection indicated that there was still contamination by normally reddened stars, and the limits were moved by 0.2 mag toward the red compared with the formulas given by Winston et al. (2007) and and Jose et al. (2016). To then isolate Class I and Class II YSOs from these sources, we used the Phase II color selection from Gutermuth et al. (2009), with the CCD [KS–[3.6]]0 versus [[3.6]–[4.5]]0 as shown in Figure 7. We obtained a total of 32 Class I YSOs and a total of 385 Class II YSOs.

2019ApJ...875..129K__Kovtyukh_&_Andrievsky_1999_Instance_1

Finally, the final estimates depend slightly on whether very strong lines with X > −6 are used or not. In Figure 7, very strong lines clearly show a systematic tilt. These strong lines have an impact on the slopes, e.g., seen in Figure 4. The lower values of the stronger lines in MB99 would give higher values with a fixed ξ, but this would also cause a tilt in Figure 4. A larger ξ is therefore required so that values of strong and weak lines get balanced. While this is an important difference between the two line lists, generally speaking, it is suggested that using very strong lines often introduces complications such as non-LTE effects into a chemical abundance analysis (e.g., Kovtyukh & Andrievsky 1999; Gratton et al. 2006; Takeda et al. 2013). Based on synthetic spectra, we found that, in the case of lines with X ≳ −6, the line core does not grow any more with increasing metallicity and the damping wing starts to contribute to the EW at around the solar metallicity. If we run the bootstrap method with the same lines but including those with X > −6, we obtain moderately different results for the MB99 list, as illustrated in Figure 5. Four lines from MB99 have X > −6, and including them leads to higher ξ and lower values: = (1.47 ± 0.18, 6.94 ∓ 0.05) for Arcturus and (1.61 ± 0.16, 7.71 ∓ 0.06) for μ Leo. The changes caused by including the strongest lines are marginally significant, 1–2σ, for the former but are negligible for the latter. Figure 6 shows that one line, Fe i λ 11973.04, with the largest has a particularly strong impact on the slope in the X versus diagram for Arcturus with MB99. The same line gives ∼ 8.10 dex, which is also higher than the average, for μ Leo. However, the scatter of from lines within the low-X range is large, which explains the relatively small effect of including the high-X lines for μ Leo. In contrast, six VALD3 lines that we selected have X > −6, but including them has a negligible impact on the measurements. For VALD3, the Fe i λ 11973.046 line leads to values that are very close to the average abundances from other lines for both Arcturus and μ Leo. This line corresponds to the rightmost point in Figure 7 and has a very large difference, 0.8 dex, between the values in the two line lists. Considering these complications, we decided to adopt the values obtained without the lines at X > −6 as our best estimates. Although the from individual lines depend on ξ as described above, we found that the [Fe/H] obtained in different works are not correlated with ξ (Figure 8). This is probably because systematic differences in previous works, such as differences in line lists and atmosphere models, introduced a scatter larger than the expected correlation between the two parameters.

2021AandA...647A.137J__Beccari_et_al._2017_Instance_1

The advent of the ESA Gaia satellite for the first time allows the study of young stellar populations on large scales (> 100 pc) in six-dimensional (6D) position and velocity space. For example, using Gaia Data Release 2 (DR2) (Gaia Collaboration 2018a), Kounkel et al. (2018) and Zari et al. (2019) demonstrated that the Orion star-forming region is composed of a variety of populations with different spatial and kinematic properties each that are all likely generated in multiple events instead of in a progressive star formation history (see also Beccari et al. 2017; Jerabkova et al. 2019b; Kroupa et al. 2018, discussing three bursts of star formation in the Orion Nebula cluster). Zari et al. (2018) described the 6D properties and age structures in which young stars are found within a region of 500 pc around the Sun, and a number of other studies focusing on individual star-forming regions have been reported. One example is the large-scale picture of the Gamma Velorum region (Beccari et al. 2018; Cantat-Gaudin et al. 2019). The Gaia DR2 catalogue also led to the discovery of stellar relic filaments. These are spatial structures of a few pc that are more than 90 pc long and consist of stars of equal ages that are younger than a few hundred million years (Jerabkova et al. 2019a; Beccari et al. 2020). A Galaxy-wide survey using the Gaia DR2 catalogue reveals many elongated structures made of coeval stars, many of which are tidal tails of dissolving star clusters (Kounkel & Covey 2019). Meingast et al. (2021) described the nearby extended spatial distribution of stars that co-move with their clusters but are not bound to them. After the release of the Gaia DR2 catalogue, tidal tails have been found around four nearby ( 300 pc) OCs, namely Blanco 1 (≈100  Myr, Zhang et al. 2020), the Hyades (≈600 − 700 Myr; Meingast & Alves 2019; Röser et al. 2019; Douglas et al. 2019; Gossage et al. 2018; Reino et al. 2018; Lodieu 2020; Gaia Collaboration 2018b), Coma Berenices (≈750 Myr; Tang et al. 2019; Fürnkranz et al. 2019), and Praesepe (≈800 Myr; Röser & Schilbach 2019).

2020MNRAS.498.6013A__Tonry_1998_Instance_1

In this section, we describe our new compilation of SLS. To construct Dobs, we have chosen only systems with spectroscopically data well measured from different surveys. We have considered 19 SLS from the CASTLES, 107 from SLACS, 38 from BELLS, 4 from LSD, 35 from SL2S, and 1 system from the DES survey. The final list has a total of 204 systems, being the largest sample of SLS to date. We use spectroscopy to select those lenses with lenticular (S0) or elliptical (E) morphologies that have been modelled assuming a SIS ($\sim 3{{\ \rm per\ cent}}$) or SIE ($\sim 97{{\ \rm per\ cent}}$) lens model. Many systems have not been taken into account due to several issues. For instance, the system PG1115+080 (Tonry 1998) from the CASTLES survey has been discarded because the lens mass model is steeper than isothermal. In addition, the system MGJ0751+2716 (Spingola et al. 2018) was also discarded because the main lens belongs to a group of galaxies. From the SLACS survey (Bolton et al. 2008; Auger et al. 2009), we remove the systems SDSSJ1251−0208, SDSSJ1432+6317, SDSSJ1032+5322, and SDSSJ0955+0101 since the lens galaxies are late-type. The same reason is applied to the systems SDSSJ1611+1705 and SDSSJ1637+1439 from the BELLS survey (Shu et al. 2016). We have also discarded the systems SDSSJ2347−0005 and SDSSJ0935−0003 from the SLACS survey and the system SDSSJ111040.42+364924.4 from the BELLS survey because they have large measured velocity dispersions (∼400 km s−1 or bigger values), suggesting the lens might be part of a group of galaxies or that there is substructure in the line of sight. For those systems without reported velocity dispersion error, we assumed the average error of the measurements in the survey subsample as follows. For the nine systems from CASTLES, we consider the average error on σ for this survey, i.e. 14 per cent. In the case of the system DES J2146−0047 (Agnello et al. 2015), we have assumed a 10 per cent error on σ, which is the average error of the entire sample. The LSD survey (Koopmans & Treu 2003; Treu & Koopmans 2004) reports σ corrected by circular aperture using the expression obtained by Jorgensen, Franx & Kjaergaard (1995a,b). A close inspection of the σspec values, with and without aperture correction, presented by Cao et al. (2015) show the differences is smaller than the reported error. Thus, we decided to use the observed values (σ) and the reported error for the sample without the aperture correction. On the other hand, in those systems in which the Einstein radius error was not reported, we followed Cao et al. (2015) and assumed an error of δθE = 0.05, which is the average value of the systems with reported errors in this sample.

2019AandA...629A.134G__Groh_et_al._2008_Instance_1

The shape of the spectral energy distribution and the emission rates of ionizing photons (see Table 1 of Paper II) depend on the assumed wind mass-loss rates, wind speeds, and wind clumping. These parameters are uncertain. Theoretical predictions are now available (e.g., Krtička et al. 2016; Vink 2017), but they have not yet been thoroughly tested against observations, because only very few stripped stars with sufficiently strong wind mass-loss have been identified and studied in detail so far (e.g., Groh et al. 2008). In Paper I, we showed that variations in wind mass-loss rate primarily affect the predicted emission rate of He II-ionizing photons, while the emission rates of H I- and He I-ionizing photons are not significantly affected. The mass-loss rates assumed in our models were chosen to smoothly connect the mass-loss rates of subdwarfs (Krtička et al. 2016) with the observed mass-loss rates of WR stars (Nugis & Lamers 2000). Our assumed mass-loss rates also match well with the observed mass-loss rate of the stripped star in the binary system HD 45166 (Groh et al. 2008). The recent theoretical predictions by Vink (2017) suggest that the mass-loss rates of stripped stars may be ten times lower than what we assume in this paper. The winds of stripped stars are likely not reaching close to the Eddington limit, in contrary to massive main-sequence and WR stars (cf. Bestenlehner et al. 2014). This suggests that the wind mass-loss rate from stripped stars is lower than that from WR stars and thus not well-described by the recipe for WR stars of Nugis & Lamers (2000). To establish which are the wind mass-loss rates from stripped stars, observations of a sample of stripped stars are necessary. If, as suggested by Vink (2017), the mass-loss rates from stripped stars indeed are lower than what the recipe from Nugis & Lamers (2000) predicts, it would likely imply an increase of the emission rates of He II-ionizing photons presented in this work. The emission rates of H I- and He I-ionizing photons are robust against wind uncertainties.

2021AandA...647A.144A__Bretherton_1966_Instance_1

Understanding how inertial waves interact with co-rotation resonances is thus a key issue in quantifying tidal dissipation, especially since waves may deeply interact with the background flow at this particular location, which in turn may alter the background flow (as was proposed first by Eliassen & Palm 1961, for terrestrial mountain waves). In binary systems and for late-type stars, Goldreich & Nicholson (1989) showed that the angular momentum transported by gravity waves and exchanged at co-rotation can lead to the successive synchronisation of the layers, from the base to the top of the radiative envelope. More generally, a body of work in various domains, from astrophysical disks (e.g. Goldreich & Tremaine 1979; Baruteau & Masset 2008; Latter & Balbus 2009; Tsang & Lai 2009) to geophysical fluid dynamics (e.g. Bretherton 1966; Yamanaka & Tanaka 1984), has tried to understand the properties of wave propagation and dissipation around co-rotation and, more generally, at all special locations in fluids that correspond to singularities in the linear wave propagation equation. We will refer to them as ‘critical levels’ in the following (Maslowe 1986), or as ‘critical layers’ in the case of a viscous medium. This distinction is analogous to the distinction between shear layers and attractors of characteristics that are kinds of singularities for the governing equation of inertial waves in a spherical shell. The aforementioned singularities can act very differently, with either severe absorption at the critical level (as in Booker & Bretherton 1967, for stratified vertical shear flows) or no attenuation if the wave propagates in a peculiar direction (Jones 1967; Acheson 1972; Grimshaw 1975a, for stratifiedvertical shear flows with rotation and magnetism). In other cases, a critical level may even give rise to wave amplification under certain conditions related to the first and second derivatives of the mean flow velocity (Lindzen & Tung 1978; Lindzen & Barker 1985, for barotropic and stratified shear flows, respectively). These studies all used an invariant quantity (the Reynolds stress or thewave action for rotating or magnetic flows) as a diagnostic tool to interpret the role of the critical level in terms of energy transmission and to quantify exchanges between the wave and the mean flow (Eliassen & Palm 1961; Bretherton 1966).

2018MNRAS.473.2000T__Noutsios_et_al._2011_Instance_1

The launch of the Fermi Gamma-ray Space Telescope has spurred on the search for pulsars in γ-rays (Grenier & Harding 2015), yielding over 2001 detections and triggering multiwavelength observations. While pulsars are common targets in the X-rays, they are very challenging targets in the optical and very few of them have been identified (see Mignani et al. 2016, and references therein). Here, we report on Large Binocular Telescope (LBT) observations of an isolated pulsar, PSR J2043+2740 (Taylor, Manchester & Lyne 1993), detected by both AGILE (Pellizzoni et al. 2009) and Fermi (Abdo et al. 2010; Noutsios et al. 2011). It was discovered as a radio pulsar (Ray et al. 1996) and later on as an X-ray source by XMM–Newton (Becker et al. 2004), although X-ray pulsations have not yet been found. PSR J2043+2740 is one of the very few non-recycled pulsars older than 1 Myr detected in γ-rays, with a characteristic age τc = 1.2 Myr, inferred from the values of its spin period Ps = 0.096 s and its derivative $\dot{P}_{\rm s} = 1.27 \times 10^{-15}$ s s−1 (Ray et al. 1996). This also yields a rotational energy loss rate $\dot{E}_{\rm rot} = 5.6 \times 10^{34}$ erg s−1 and a surface dipolar magnetic field Bs = 3.54 × 1011 G.2 Although PSR J2043+2740 does not have a very large spin-down power compared to young (∼1–10 kyr) pulsars (∼1036–1038 erg s−1), it is still a factor of 2 larger than that of middle aged γ-ray pulsars (∼0.1–0.5 Myr), such as Geminga, PSR B0656+14 and PSR B1055−52, all detected in the optical, thanks to their distances ≲ 500 pc (Abdo et al. 2013). The distance to PSR J2043+2740 is uncertain owing to the lack of a radio parallax measurement. The radio dispersion measure (DM = 21.0 ± 0.1 pc cm−3; Ray et al. 1996) gives a distance of 1.8 ± 0.3 kpc from the NE2001 model of the Galactic-free electron density (Cordes & Lazio 2002). A slightly smaller distance (1.48 kpc) is inferred from the model of Yao, Manchester & Wang (2017). The hydrogen column density towards the pulsar obtained from the X-ray spectral fits (NH ≲ 3.6 × 1020 cm−2; Abdo et al. 2013) suggests a distance of a few hundred pc (He, Ng & Kaspi 2013), although these estimates depend on the model X-ray spectrum. Such a distance would make PSR J2043+2740 a viable target for deep optical observations, never carried out until now, and might be compatible with the debated association (Noutsios et al. 2011) with the Cygnus Loop supernova remnant (SNR) at $540^{+100}_{-80}$ pc (Blair, Sankrit & Raymond 2005).

2015AandA...582A..88W__Lamberts_et_al._2013_Instance_1

For completeness, we also supplement our reaction scheme with grain-surface association reactions extracted from the publicly available Ohio State University (OSU) network3 (Garrod et al. 2008). For those species important in grain-surface chemical reaction schemes, e.g., the CH3O radical, which are not included in Rate12, we also extract the corresponding gas-phase formation and destruction reactions from the OSU network. The grain-surface network has been further updated to include all studied routes to water formation under interstellar and circumstellar conditions (Cuppen et al. 2010a; Lamberts et al. 2013). The grain-surface reaction rates are calculated assuming the Langmuir-Hinshelwood mechanism only, and using the rate-equation method as described in Hasegawa et al. (1992). We limit the chemically “active” zone to the top two monolayers of the ice mantle. We assume the size of the barrier to surface diffusion is 0.3× the binding energy; in this way, volatile species diffuse at a faster rate than strongly bound species. This value lies at the optimistic end of the range determined by recent off-lattice kinetic Monte Carlo simulations of surface diffusion of CO and CO2 on crystalline water ice (Karssemeijer & Cuppen 2014). This allows the efficient formation of complex organic molecules via radical-radical association reactions at ≳20 K (see, e.g., Vasyunin & Herbst 2013; Walsh et al. 2014). For the lightest reactants, H and H2, we use either the classical diffusion rate or the quantum tunnelling rate depending on which is fastest (see, e.g., Tielens & Hagen 1982; Hasegawa et al. 1992). For the latter rates, we follow Garrod & Pauly (2011) and adopt a rectangular barrier of width 1.5 Å. We also include reaction-diffusion competition in which the reaction probability is determined by the relative rates between the barrier-mediated reaction and thermal diffusion (see, e.g., Chang et al. 2007; Garrod & Pauly 2011). Although still relatively simplistic, this takes into account the increased probability of reaction in the limit where the thermal diffusion of the reactants away from a common binding site is slow compared with the barrier-mediated reaction rate.

2018AandA...612A..77M__Gromadzki_&_Mikołajewska_(2009)_Instance_3

“Wiggling” outflows are often observed among young stellar jets and protostellar molecular outflows (Eisloffel et al. 1996; Terquem et al. 1999). Terquem et al. (1999) investigated such binary systems where the accretion disk, from which the jet originates, is inclined to the binary orbital plane. They concluded that the observed jet “wiggling” is a consequence of the jet precession caused by tidal interactions in such non-coplanar binary systems. Nichols & Slavin (2009) as well as Hollis & Michalitsianos (1993) suggested that the precession of the accretion disk around the WD may be responsible for the bending of the wide-angle outflow found in the previous studies. In analogy to these observations of young stellar jets, we suggest that the “wiggling” that we also find here for the R Aqr jet may result from disk precession as well. We estimated the precession period using the theory from Terquem et al. (1999) and the binary parameters taken from Gromadzki & Mikołajewska (2009) – Mh = 0.8M⊙ (the mass of the hot WD companion), Mp∕Mh = 1.65 (where Mp is the mass of the primary companion), D = 15.5 AU (the mean semi-major axis of the system), and e = 0.25 (eccentricity). The value of Rd is unknown in our case and we used Rd = 5 AU giving D∕Rd ≈ 3 which corresponds to the average value of 2 ≤ D∕Rd ≤ 4 (the range taken from Terquem et al. 1999). For this calculation, we assumed that the angle δ between the disk plane and that of the binary orbit is small enough (10°) and we adopted cosδ = 1. Using Eq. (1) from Gromadzki & Mikołajewska (2009), we derived the precession time of T ≈ 530 yr. This value is quite large for the wiggling waves that we see. We estimated the projected spatial wavelength λproj of the “wiggling” wave according to λ = λproj∕sin i, where i is the angle between the jet symmetry axis and the line of sight, and T = λ∕υ, where υ is the jet velocity, from Gromadzki & Mikołajewska (2009). Using i = 72° and υ ~ 100 km/s, we derive λproj ≈ 10 500 AU which is more than 20 times larger than the projected length of the observed wiggling outflow (2′′ ≈ 440 AU). However, we should note that the precessing time strongly depends on the D∕Rd; the T decreases significantly with increasing R. It may also be the case that the “wiggling” model developed for YSO jets is not fitting for R Aqr which consists of evolved objects, and both the WD and the disk where the jet probably forms are much hotter than YSO systems. Furthermore, we cannot exclude that the steady wiggling might be a sequence of dynamical interactions of the two collimated flows tilted to each other.

2016MNRAS.463.3783B__Reid_&_White_2011_Instance_1

It is well known that a per cent level understanding of the anisotropy of the redshift-space galaxy clustering is needed to accurately recover cosmological information from the RSD signal in order to shed light on the issue of dark energy versus modified gravity. From a statistical point of view, the source of the anisotropy is the galaxy line-of-sight pairwise velocity distribution. It is therefore important to adopt a realistic functional form for this velocity PDF when fitting models to the data. To this purpose, in Paper I we introduced the GG prescription for the velocity PDF. In this work, we have continued the development of this model by making explicit the dependence of the GG distribution on quantities predictable by theory, namely its first three moments, and extending it to the more general concept of GQG. To keep the model as simple as possible, we have proposed an ansatz with two free dimensionless parameters that describe how infall velocity and velocity dispersion vary when moving from one place to another in our Universe. Since their interpretation is clear, these parameters can be theoretically predicted or, assuming a more pragmatic approach, tuned to simulations or used as nuisance parameters. State-of-the-art PT has proven successful in predicting the large-scale behaviour of the velocity PDF and the correspondent monopole and quadrupole of the redshift-space correlation function (e.g. Reid & White 2011; Wang, Reid & White 2014), at least for massive haloes, M ∼ 1013 M⊙. Unfortunately, by definition, any PT breaks down for small separations. Consequently, alternative approaches have been suggested in the literature, spanning from purely theoretical (e.g. Sheth 1996) to hybrid techniques in which N-body simulations plus an HOD are employed to deal with the issue of non-linearities (e.g. Tinker 2007; Reid et al. 2014). One of the main results from our work is to provide a framework in which perturbation and small-scale theories are smoothly joined, so that all available RSD information can be coherently extracted from redshift surveys. A fundamental requirement for a redshift-space model is that it must be precise on all scales interest, and it should inform the user of the scales on which the model can be trusted. We have compared to N-body simulations the well-known GSM (Reid & White 2011), the more recent ESM (Uhlemann et al. 2015) and the GQG prescription over a broad range of separations, from 0 to 80 h−1 Mpc. Different redshifts, from z = 0 to 1, and different tracers, namely DM particles and two mass-selected catalogues of DM haloes, have been considered. We have concluded that, among the three, QGQ is the only model capable of providing a precise redshift-space correlation function on scales down to ∼5 h−1 Mpc over the range of redshifts covered by future surveys. Keeping in mind that the range of validity of the models depends on tracer, redshift and order of the Legendre multipoles we are interested in, for finiteness, we can say that all the models converge to the expected amplitude on scales ≳30 h−1 Mpc, at least for multipole and quadrupole. Since these scales roughly coincide with the range of validity of state-of-the-art PTs, if we rely only on PT and if we are not interested in higher order multipoles, the most natural choice is the simplest model among the three, i.e. the GSM. As for the ESM, we have found it to be unbiased down to smaller scales and for higher order multipoles than the GSM, thus confirming the results by Uhlemann et al. (2015), but, on the other hand, it seems to behave even worse than the GSM on the smallest scales. We can therefore think of it as a natural extension of the GSM in the perspective of further PT developments. In particular, a better prediction of the third moment of the velocity PDF is required before the ESM can be applied to data on smaller scales. Formally, the same argument holds for the GQG model, none the less, since this latter is meant to include non-linear scales, it could be possible to obtain a prediction for the third moment by interpolating between (very) small and (very) large scales. More precisely, as shown in the lower-right panel of Fig. A1, the functions $c^{(3)}_t$ and $c^{(3)}_r$, which fully characterize the third moment, are peaked at r ≲ 10 h−1 Mpc. By adopting a model for the small-scale limit that includes those separation, most likely using simulations in a similar way to that proposed in Reid et al. (2014), we would then be able to interpolate between these peaks and their large-scale limit, which is trivially 0.

2021MNRAS.507.4389G__Masters_et_al._2011_Instance_3

Erwin (2018) showed that, in a sample drawn from the Spitzer Survey of Stellar Structure in Galaxies (S4G), the bar fraction is constant over a range of (g −r) colours and gas fractions. Their bar fraction does not increase, but rather decreases for stellar masses higher than ∼ 109.7M⊙. These results are in contrast to many SDSS-based studies cited above. Erwin (2018) argues that this apparent contradiction can be explained if SDSS-based studies miss bars in low-mass blue galaxies. In Figs 5 and 6, we showed that the newly detected bars in GZD (compared to GZ2) are weak bars in low-mass blue galaxies. Nevertheless, the ‘combined’ bar fraction in Fig. 6 is not constant over (g −r) colour and agrees well with Masters et al. (2011) for redder colours [(g −r) colour > 0.5]. Additionally, our ‘combined’ bar fraction remains roughly constant over stellar mass. As mentioned before, we conclude that strong bars drive the trends of bar fraction with (g −r) colour, stellar mass, and SFR observed in other studies (Nair & Abraham 2010b; Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017). However, the addition of weak bars in low-mass blue galaxies is insufficient to resolve the apparent disagreement between Erwin (2018) and many SDSS-based studies (Masters et al. 2011, 2012; Vera et al. 2016; Cervantes Sodi 2017; Kruk et al. 2018), which instead seems likely to be due to the very different sample selection of the S4G and SDSS galaxy samples. For example, the median stellar mass of the sample used in Erwin (2018) is ∼109.6M⊙ (based on their Fig. 4 and the bins in the top left-hand panel of their Fig. 5). However, the median stellar mass of our sample is 1010.6M⊙. As stellar mass correlates with many parameters (including bar length), this can have major consequences. Additionally, as Erwin (2018) notes, there is also the issue of resolution to consider. With an r-band FWHM of 1.18 arcsec from DECaLS (Dey et al. 2019) and a mean redshift of 0.036, the mean linear resolution of our sample is approximately 834 pc, which is higher than the 165 pc of Erwin (2018). This explains why they observe many sub-kpc bars, while we do not. These differences in stellar mass and resolution will manifest themselves in the conclusions, so a more detailed analysis is needed for a proper comparison with Erwin (2018).

2020AandA...644A..59K__Heays_et_al._2017_Instance_1

Analyzing optical emission lines, emanating from within the northern lobe, Tylenda et al. (2019) found a reddening with EB − V ≈ 0.9 mag or AV ≈ 2.8 mag, which we assume is mainly circumstellar in origin. Hajduk et al. (2013) observed two stars shining through the southern lobe and found AV = 3.3 − 4.4 mag with unknown contribution from the interstellar component. We assume here that those observations quantify the amount of circumstellar dust that is the main actor in shielding molecules from the central source. We recalculated the lifetimes of molecules assuming AV = 3 mag, and with (1) standard dust properties (i.e. with composition and size distribution as of interstellar dust) or (2) with larger and less opaque grains, at the gas-to-dust mass ratio of 124 (see Heays et al. 2017, for more details on the assumed dust properties). We used shielding functions from Heays et al., which include effects in lines. Results are shown in Cols. (3) and (4) of Table 3. The presence of ISM grains makes it possible for the observed molecules to survive for a very long time, longer than 350 yr. The lifetimes in the presence of the large grains considered by Heays et al. are typically a few times shorter than the age of the remnant. It is uncertain what kind of grains populate the dusty remnant of CK Vul, but given its anomalous elemental and molecular compositions and eruptive history, dust may have a peculiar chemical composition and size distribution. In such a case, the total to selective extinction law would also be different and the assumed AV may not be adequate. Nevertheless, if the molecules formed 350 yr ago and are shielded by big grains, with the calculated lifetimes a considerable fraction of molecular species would survive, except perhaps for a few most fragile ones which indeed are almost absent in the lobes. We conclude that the lifetimes in Table 3 that were calculated with an attenuated ISRF are consistent with the molecule formation 350 yr ago or more recently.

2020MNRAS.493.3045B__Jaisawal_&_Naik_2015a_Instance_3

We have used 3.0–75.0 keV NuSTAR data to probe any cyclotron line feature. To describe the continuum of 4U 1700–37, we have applied the NPEX model [cons*TBpcf*(powerlaw*npex+gaus+gaus)], following the previous work of Jaisawal & Naik (2015a). The NPEX model has been created by adding two cutoffpl models with their cutoff energies tied to each other and keeping the photon index of one to be frozen at –2.0. For the best fit, the χ2/d.o.f is found to be 577.64/475. The fit shows some residuals in the overall spectrum. We added a Gaussian absorption model around 39 keV (following the previous work of Jaisawal & Naik 2015a), but the best fit gives the line energy as $15.44^{+0.56}_{-0.53}$ keV with χ2/d.o.f = 487.32/472. The width and the depth of the line are found to be $5.47^{+0.90}_{-0.78}$ keV and $1.29^{+0.51}_{-0.39}$, respectively. The chance probability of the line has been computed using the ftest task in xspec. The F-test with this absorption line gives an F value = 29.2 and a chance probability of 2.61 × 10−17(Table 3). If we add another Gaussian absorption line at 38.9 keV (Energy value frozen) the best-fitting χ2/d.o.f is found to be 486.8/470. This indicates that the second absorption line is not required for the fit. If we use only one Gaussian absorption line and freeze the line energy at 38.9 keV then the width and the depth of the line are found to be $4.54_{-0.87}^{+0.90}$ keV and $2.87_{-1.05}^{+1.24}$, respectively with a χ2/d.o.f = 549.2/473. The ftest gives a chance probability of the 38.9 keV line to be 6.4 × 10−6. So, with NPEX model we find two valid model combinations of the data. One, the presence of a Gaussian absorption line at ∼15 keV, two, the presence of a Gaussian absorption line at 38.9 keV. But, the presence of both lines together is not supported by the data. The 10.0–70.0 keV flux of the source is found to be (2.26 ± 0.01) × 10−9 erg cm-2s-1, much lower than the value (5.6 ± 0.3) × 10−9 erg cm-2s-1, previously reported from SUZAKU data (Jaisawal & Naik 2015a).

2019AandA...627A.114K__Robitaille_2010_Instance_1

In the main simulation, we modeled a Cartesian cube 2000 AU in size with 150 grid points in each direction. The cell size of 13.3 AU is comparable to the size of the star, which was represented by a point source. Clumps A to D were simulated as 3D structures of a Gaussian density profile. The choice of a Gaussian distribution was dictated only by the simplicity of the implementation. Whereas observations constrain the clump sizes and overall relative location in two spatial dimensions, the extent of a given feature along the line of sight was a free parameter. We included scattering in the radiative transfer calculations. It turned out early on in the simulations that the dusty medium is very optically thick for visual and ultraviolet (UV) photons making the calculations particularly time-consuming. To speed them up, we used the Modified Random Walk method implemented in RADMC-3D (Robitaille 2010). The number of photons used in the Monte Carlo simulations varied from 105 to 109. For each spatial configuration of dust distribution, first a thermal structure was calculated. Based on this calculation, regions where the dust temperature exceeded the sublimation temperature were removed from the simulation and the thermal structure was calculated once again. In this way we reconstructed images that were further processed in CASA for a direct comparison to ALMA continuum maps. Given a very high number of free parameters (over 40 in the basic version of the simulation), the best configurations were searched through iterative trial-and-error modifications of the models. We aimed to construct only a general 3D model of the dusty environment requiring a minimum dust mass possible. We attempted to reproduce the peak and integrated ALMA fluxes of each feature within the order of magnitude. This aim, however unambitious it may appear, was challenging without increasing further the number of free parameters. For example, better fits to total flux and its distribution would require considering density distributions other than Gaussian.

2015ApJ...800...24K___2012_Instance_1

Analyzing the fraction of quenched galaxies for centrals and satellites as a function of stellar mass and environmental parameters has emerged as a fruitful way of gaining insights into the phenomenology and the physical processes of quenching. However, one of the difficulties in interpreting this fraction is that many of the environmental parameters are statistically correlated both with each other or with the stellar mass of the galaxy. For example, there is good evidence that halo mass correlates with the mass of the central galaxy (e.g., Yang et al. 2009), that the local overdensity correlates with the group-centric distance (e.g., Peng et al. 2012; Woo et al. 2013), and that the local overdensity also correlates with halo mass (e.g., Haas et al. 2012; Carollo et al. 2013b). These correlations among the parameters can introduce spurious dependencies, i.e., dependencies which have no direct causal relation to quenching, if the quenched fraction is regarded as a function of certain parameters in isolation. Therefore, in principle one has to study the quenched fraction in the full parameter space and to vary only one parameter at a time, while keeping the others fixed (see, e.g., the discussion in Mo et al. 2010; Section 15.5). However, this approach does also not necessarily guarantee that the measured dependence of the quenched fraction on a certain parameter is directly related to a physical quenching process, since the red fraction may also depends on the history of the galaxy, i.e., how the galaxy is moving through the parameter space. Therefore, interpreting trends of the quenched fraction even in the full parameter space has turned out to be quite difficult and has led to some confusing and apparently inconsistent statements in the literature. In this paper, we use the Sloan Digital Sky Survey (SDSS; York et al. 2000) and the group catalog of Yang et al. (2012) to take a new look at some of the issues that have been recently raised.

2015ApJ...813...20B__Kim_2001_Instance_1

In our investigation we were looking for alignment of galaxy clusters in a sample of 1056 low redshift ACO clusters with known Bautz–Morgan type. We found a statistically significant effect for structures with separation distance 30 R ≤ 45 h−1 Mpc. This allowed us to conclude that, for the analyzed clusters, the effect has a range to about 45 h−1 Mpc. We found a stronger alignment of elongated clusters of BM type I (an excess of small values of the Δθ angles is observed), having a range to about 45 h−1 Mpc. The alignment for BM I is probably connected with the origin of a supergiant galaxy. One should note that during studies of the isolated Abell groups (Flin & Olowin 1991; Trevese et al. 1992; Kim 2001; Niederste-Ostholt et al. 2010), only a rudimentary alignment was found and related only to the brightest cluster members. Gonzalez-Sanchez & Teodoro (2010) interpreted the alignment of just the brightest galaxies within a cluster as the effect of gravitational tidal forces. Correlation between the orientation of the brightest galaxy within a cluster and the cluster’s large axis was also found by Sastry (1968), Carter & Metcalfe (1980), Binggeli (1982), Struble & Peebles (1985), Rhee & Katgert (1987), West (1989, 1994), van Kampen & Rhee (1990), Plionis (1994), Fuller et al. (1999), and Kim et al. (2002). The role of a much more massive central galaxy is related to the problem of galaxy mergers in a cluster. This, in turn, is due to the fact of (generally speaking, random) interaction between cluster members and the dynamic evolution of structures near to specific cluster members. The problem of dynamic evolution can be studied by combined studies of the Binggeli effect, i.e., the relation between the positions of major axes in groups or clusters of galaxies and the directions toward their neighbors, and of the mutual orientation of the brightest galaxy (and other bright galaxies) in a structure relative to the position of cluster major axes or even examination of structure ellipticity redshift dependence, especially in the enlarged observational samples.

2018AandA...617A..86L__Tian_2017_Instance_1

The IRIS spectra measure the flare in a “sit-and-stare” mode with a roll angle of 45∘. The spectral scale is ∼25.6 mÅ per pixel in the far-ultraviolet (FUV) wavelengths. The IRIS slit crosses the flaring loop and one ribbon (Fig. 1). Two red bars enclose the flaring loop region used to study the quasi-periodic oscillations in this work. IRIS spectrum was pre-processed with the SSW routines of “iris_orbitval_corr_l2.pro” (Tian et al. 2014; Cheng et al. 2015) and “iris_prep_despike.pro” (De Pontieu et al. 2014). To improve the signal-to-noise ratio, we apply a running average over five pixels to the IRIS spectra along the slit (Tian et al. 2012, 2016). We also manually perform the absolute wavelength calibration using a relatively strong neutral line, O I 1355.60 Å (see De Pontieu et al. 2014; Tian et al. 2015; Tian 2017). IRIS observations show that Fe XXI 1354.08 Å is a hot (∼11 MK) and broad emission line and is always blended with many narrow chromospheric lines at the flaring ribbons (Li et al. 2015b, 2016b; Tian et al. 2015; Young et al. 2015; Brosius et al. 2016; Polito et al. 2016). However, the Fe XXI 1354.08 Å line is much stronger than those blended emission lines at the flaring loops (Tian et al. 2016). Figure 2a gives the time evolution of the line profiles of Fe XXI 1354.08 Å, averaged over the slit positions between ∼18.3″ and 21.6″. Figure 2 panels b−f show the spectral line profiles at the time indicated by the yellow lines in panel a. We can see that only the cool line of C I 1354.29 Å is blended with the hot line of Fe XXI 1354.08 Å, but its contribution is negligible. Therefore, double Gaussian functions superimposed on a linear background are used to fit the IRIS spectra at “O  I” window (Tian et al. 2016). Next, we can extract the hot line of Fe XXI 1354.08 Å, as shown by the turquoise profile. The purple profile is the cool line of C I 1354.29 Å. Two orange peaks represent the cool lines of O I 1354.60 Å and C I 1354.84 Å (Tian 2017), which are far away from the flaring line of Fe XXI 1354.08 Å. Finally, the line properties of Fe XXI 1354.08 Å are extracted from the fitting results, that is, Doppler velocity, peak intensity, and line width (Li et al. 2016b; Tian et al. 2016; Tian & Chen 2018).

2018ApJ...853...50F__Bernard_et_al._2015b_Instance_3

However, using the well-assessed new post-AGB evolutionary models, we confined the main-sequence ages of our halo sample to be mostly ∼2–5 Gyr, with the oldest being ∼6–8 Gyr, while the outer-disk sample are mostly ≲1–4 Gyr. We thus conjecture that our targets probably formed prior to the encounter with M33. Obviously, our sample represents the population that is different from the underlying, smooth, extended (and mostly metal-poor) halo component of M31 (Ibata et al. 2007, 2014), which was formed through the repeated accretion of smaller galaxies in the distant past. These bright PNe seem to resemble the younger, metal-rich population in the outer stream of M31, as revealed by HST pencil-beam pointings on the Giant Stream (Brown et al. 2006a; Bernard et al. 2015b). The metallicity of the stream fields was enriched continuously from [Fe/H] ∼ −1.5 to at least solar level about 5 Gyr ago (Bernard et al. 2015b). This timeline of metal enrichment is generally consistent with the stellar ages of our metal-rich sample. N-body simulations suggested that the Giant Stream and other stream-like features in the halo are debris of a massive (≳109– ) progenitor that was recently disrupted during the course of a merger (e.g., Ibata et al. 2004; Fardal et al. 2006, 2007, 2008, 2013; Font et al. 2006; Geehan et al. 2006; Mori & Rich 2008; Sadoun et al. 2014). The extended star formation history and the broad range of metallicity (−1.5 ≲ [Fe/H] ≲ 0.2) discovered in the stream fields can be explained by a disk galaxy progenitor (Brown et al. 2006a, 2006b; Bernard et al. 2015b). If the stellar streams in M31's halo indeed have a common origin, our sample of halo PNe then probably formed through extended star formation in this possibly massive, disk-like progenitor. Moreover, some simulations predict that the remnant of the disrupted satellite resides in the NE Shelf (e.g., Fardal et al. 2008, 2013; Sadoun et al. 2014); PN17 in our sample is located in this region and might be associated with this substructure (see Section 4.4).

2020AandA...641A.151S__Spinoglio_et_al._(2002)_Instance_1

The galaxy NGC 7213 benefits from a detailed SED decomposition performed by G16, which allowed us to disentangle the relative contributions of AGN and SF activity to the global IR outcome of the source, providing a characterisation of the host galaxy in terms of stellar and dust content (M⋆ and Mdust, respectively), and ongoing SF (SFR). Here, we briefly introduce the photometric data collected from the archive and the SED decomposition procedure adopted by G16. The homogenised catalogue of total fluxes, from the UV to the FIR, is presented by G16 (see also their Table A.1: the flux densities are corrected for the aperture and magnitude zero point). In the case of NGC 7213, the photometric data included in the analysis are: the U, B, V, and R bands from de Vaucouleurs et al. (1991); the near-infrared measurements from the catalogue by Jarrett et al. (2000); the IRS spectrum re-binned by Gruppioni et al. (2016) and the photometry by Gallimore et al. (2010) and Moshir et al. (1990) in the MIR; and the FIR photometry by Spinoglio et al. (2002). The adopted SED fitting code was SED3FIT5 (Berta et al. 2013), which reproduces the stellar emission and the emission of the dust heated by the stars and the torus emission simultaneously. The code used the library by Bruzual & Charlot (2003) for the stellar contribution, that of da Cunha et al. (2008) for the IR dust-emission, and the library of smooth AGN tori by Fritz et al. (2006), updated by Feltre et al. (2012). In order to limit the degeneracy among the torus parameters, in G16 the AGN configurations of obscured sources were excluded for NGC 7213 (as supported by optical observations of the source and by the X-ray spectral properties presented in Sect. 3.1.2). The best fit model and the decomposition in the different components is presented in Fig. 5. The host-stellar contribution and the dusty SF dominate over the AGN in the optical bands and in the entire IR band, respectively. While this could appear to be in contrast with the type 1/broad-line nature of the AGN, it is in agreement with the relatively weak nuclear activity observed in NGC 7213 (revealed also through the X-ray spectral analysis reported Sect. 3.1.2).

2020ApJ...897...84T__Zhang_et_al._2019_Instance_1

A comparison between the constraints in Figure 2 and those found from the analysis of the reflection spectrum of the disk of other sources in previous studies is not straightforward because these measurements are quite sensitive to the specific source and the quality of the data, so it may be dangerous to generalize the results found from our analysis of 17 RXTE observations of LMC X-1. In general, a correlation between the estimates of a* and α13 is common even when we analyze the reflection spectrum. However, such a degeneracy can be broken when the inner edge of the disk is very close to the compact object (Tripathi et al. 2018, 2019b; Zhang et al. 2019). For example, Tripathi et al. (2019a) analyzed simultaneous XMM-Newton and NuSTAR observations of MCG–6–30–15 obtaining and (90% confidence level for one relevant parameter). Even if there is a correlation between these measurements of a* and α13, see Figure 6 in Tripathi et al. (2019a), we can get quite stringent constraints on both parameters. This is not the case with the analysis of the thermal component presented in this paper. When we assume the Kerr metric, observations 7, 12, and 14 of LMC X-1 give quite high spin values (see left column of Table 1), comparable to the XMM-Newton and NuSTAR observations of MCG–6–30–15. However, when we leave α13 free it is not easy to constrain a* and α13 at the same time. While the limited energy resolution of RXTE with respect to XMM-Newton may have some effect, the key-point is in the difference between the reflection spectrum and the thermal one. The former is characterized by many features, notably, but not only, the iron Kα complex around 6–7 keV. Such features help to break the parameter degeneracy, even if the reflection spectrum has several parameters to fit. The thermal spectrum, on the contrary, has quite a simple shape and there is an intrinsic degeneracy among the model parameters. This is true even when we assume the Kerr metric: it is possible to measure the black-hole spin only when we have independent estimates of the black-hole mass, distance, and inclination angle of the disk. If we want to use the continuum-fitting method to test the Kerr metric and we add a deformation parameter, the problem of degeneracy between a* and α13 should not be a surprise.

2016MNRAS.462.3441D__Namouni_1999_Instance_4

In principle, Fig. 5, central panel G, shows that (469219) 2016 HO3 may have been locked in a Kozai–Lidov resonance with ω librating about 270° for nearly 100 kyr and probably more. Because of the Kozai–Lidov resonance, both e (central panel E) and i (central panel F) oscillate with the same frequency but out of phase (for a more detailed view, see Fig. 4, panels E and F); when the value of e reaches its maximum the value of i is the lowest and vice versa ($\sqrt{1 - e^2} \cos i \sim$ constant, see Fig. 4, panel B). During the simulated time and for the nominal orbit, 469219 reaches perihelion and aphelion the farthest possible from the ecliptic. Fig. 5, G-panels, show that for other incarnations of the orbit of 469219, different from the nominal one, ω may librate about 90° as well during the simulated time interval. However, is this a true Kozai–Lidov resonance? Namouni (1999) has shown that the secular evolution of co-orbital objects is viewed more naturally in the erωr-plane, where er and ωr are the relative eccentricity and argument of perihelion computed as defined in Namouni's work (see equations 3 in Namouni 1999); these are based on the vector eccentricity and the vector inclination. Fig. 6 shows the multi-planet erωr-portrait for the nominal orbit of this object. It clearly resembles figs 13 and 19 in Namouni (1999). Asteroid 469219 librates around $\omega _{\rm r}=-90{^\circ }$ for Venus, the Earth, and Jupiter. This behaviour corresponds to domain III in Namouni (1999), horseshoe-retrograde satellite orbit transitions and librations (around $\omega _{\rm r}=-90{^\circ }$ or 90°). For a given cycle, the lower part corresponds to the horseshoe phase and the upper part to the quasi-satellite or retrograde satellite phase. This is not the Kozai–Lidov resonance; in this case, the Kozai–Lidov domain (domain II in Namouni 1999) is characterized by libration around $\omega _{\rm r}=0{^\circ }$ (or 180°) which is only briefly observed at the end of the backwards integrations (see Fig. 6). The Kozai–Lidov resonance is however in action at some stage in the orbits displayed in Figs 5 and 8. Our calculations show that the orbital evolution followed by 469219 is the result of the dominant secular perturbation of Jupiter as the periodic switching between co-orbital states ceases after about 8 kyr if Jupiter is removed from the calculations. Fig. 7 shows that, without Jupiter, 469219 switches between the Kozai–Lidov domain and that of horseshoe-quasi-satellite orbit transitions and librations (including both −90°and 90°). Jupiter plays a stabilizing role in the dynamics of objects following orbits similar to that of 469219. It is not surprising that Jupiter instead of the Earth or Venus is acting as main secular perturber of 469219. Ito & Tanikawa (1999) have shown that the inner planets share the effect of the secular perturbation from Jupiter; in fact, Venus and our planet exchange angular momentum (Ito & Tanikawa 2002). In their work, these authors argue that the inner planets maintain their stability by sharing and weakening the secular perturbation from Jupiter. Tanikawa & Ito (2007) have extended this analysis to conclude that, regarding the secular perturbation from Jupiter, the terrestrial planets form a collection of loosely connected mutually dynamically dependent massive objects. The existence of such planetary grouping has direct implications on the dynamical situation studied here; if Jupiter is removed from the calculations, the overlapping secular resonances and the recurrent dynamics disappear as well.

2016MNRAS.458...84A__Larsson_et_al._2007_Instance_1

While the properties of the SLSN hosts themselves are of interest, they are most diagnostic when compared to other classes of extragalactic transient whose progenitors are better understood. To this end, we employ a comparison sample of LGRB and CCSN3 host galaxies. In principle, CCSNe should trace all core-collapse events, although the mass function means they will be dominated by stars at the lower mass end (∼8 M⊙ to ∼25 M⊙). There also remains a possibility that some very massive stars can undergo core collapse without yielding a LSNe (e.g. Smartt 2009; Ugliano et al. 2012; Kochanek 2014) such that CCSNe samples might only provide a census of lower mass core collapsing stars (e.g. 8 M* 20 M⊙). Indeed, constraints from explosion parameters have shown the majority of CCSNe to be consistent with lower mass progenitors, as opposed to more massive Wolf–Rayet stars (Cano 2013; Lyman et al. 2016) GRBs likely represent a population with rather larger initial masses (Larsson et al. 2007; Raskin et al. 2008). LGRBs are now known to be associated with the core collapse of massive stars, and broad line SN Ic are near ubiquitously associated with low-z events (where such signatures can be seen; Hjorth et al. 2012). When compared to the hosts of CCSNe they are generally smaller and of lower luminosity, consistent with an origin in galaxies of lower metallicity (Fruchter et al. 2006; Svensson et al. 2010). In relatively local examples, where spatially resolved gas phase metallicities can be obtained, these indeed appear to be lower for GRBs than for CCSNe, even in cases where the luminosity of the galaxy is relatively high (i.e. the GRB host galaxies lie off the mass–metallicity relation; Modjaz et al. 2008; Graham & Fruchter 2013). Hence, comparing the hosts of SLSNe to these events allows us to test the large-scale environments of SLSNe against those of the bulk core-collapse population and a subset which appears to derive largely from massive stars at lower metallicity, although we note that agreement on this matter is not complete (e.g. Podsiadlowski, Joss & Hsu 1992; Eldridge, Izzard & Tout 2008; Smartt 2009; Drout et al. 2011). By exploiting both LGRB and CCSN host samples we can ascertain if there is a strong metallicity dependence in SLSN production, and if this is more or less extreme than that observed in GRB hosts.

2015AandA...582A..41H__Davis_et_al._2014_Instance_1

The snowlines of various volatiles (sublimation temperature Tsub ≲ 160 K) play a major role for planet formation. Beyond the snowline, the high abundances of solids allow for efficient sticking to form larger bodies, which is further enhanced by the presence of ices (e.g., Stevenson & Lunine 1988; Ros & Johansen 2013). Extensive studies have investigated the snowline in protoplanetary disks around pre-main-sequence stars similar to the nebula out of which the solar system is assumed to have formed (e.g., Lissauer 1987; Pollack et al. 1996). In such models, the water snowline is located at a radius of a few AU. It is thought that the early pre-solar nebula was hot (>1500 K), such that both volatiles and refractories (Tsub ≳ 1400 K) are in the gas phase out to larger distances (Cassen 2001; Scott 2007; Davis et al. 2014; Marboeuf et al. 2014). The evidence of such a hot solar nebula comes from the history of the refractories, but the volatile content of comets seems to indicate that a part of the disk remains cold (Bockelée-Morvan et al. 2000; Mumma & Charnley 2011; Pontoppidan et al. 2014). The evolution of the snowline due to disk and star evolution and its accretion rate clearly affects the chemical composition in the region relevant to planet formation (e.g., Lodders 2004; Davis 2005; Öberg et al. 2011b). The most relevant volatiles are the known major ice species: H2O, CO2, and CO. Observations (Meijerink et al. 2009; Zhang et al. 2013) and models (e.g., D’Alessio et al. 1998; Dullemond et al. 2007) of protoplanetary disks around pre-main sequence T Tauri stars indicate that such disks are not warm enough to have gas-phase volatiles in the midplane beyond 30 AU, as claimed in some early solar nebula models, and, for the case of H2O, a snowline of only a few AU is typically found. Higher temperatures at large radii might be achieved, but only during the deeply embedded phase of star formation when the accretion rate is high. The question remains how hot an embedded accreting disk can be when the accretion rate is high (≥10-6M⊙ yr-1, see Dunham et al. 2014, for a recent review).

2021MNRAS.506.2181L__Qi_et_al._2019_Instance_1

There are still several sources of systematics we do not consider in this paper. For instance, whether the use of a different mass distribution models for these lenses could significantly affect the final result. Therefore, we performed a sensitivity analysis and repeated the above calculation using the extend power law (EPL) lens model, in which the luminosity density profile [v(r) ∼ r−δ] is different from the total mass (luminous plus dark matter) density profile ρ(r) ∼ r−α. Such lens model has found widespread astrophysical applications in the literature (Cao et al. 2016a; Xia et al. 2017; Qi et al. 2019), considering the anisotropic distribution of stellar velocity dispersion β (Koopmans 2005; Cao et al. 2017b; Chen et al. 2019). With the EPL lens parameters (α, β, δ) modelled by Gaussian distributions α = 2.00 ± 0.08, δ = 2.40 ± 0.11, and β = 0.18 ± 0.13 (Gerhard et al. 2001; Bolton, Rappaport & Burles 2006; Graur et al. 2014; Schwab, Bolton & Rappaport 2010), the scatter plot of the deviation Tzs in EPL model is shown in Fig. 4. Our results provide the deviation Tzs = 0.974 ± 0.017 [corresponding to c(zs) = 2.922(± 0.051) × 105 km s−1], and the median value Med(Tzs) = 0.983 with the median absolute deviation MAD(Tzs) = 0.259 for the full lens sample. Therefore, our results show that the assumed lens model has a slight impact on the SOL constraint, which highlights the importance of auxiliary data (such as more high quality integral field unit) in improving constraints on the density profile of gravitational lenses. More detailed models of mass distribution, such as Navarro–Frenk–White density profile (suitable for dark matter distribution; Navarro, Frenk & White 1997), Sersic-like profile (suitable for stellar light distribution) (Sérsic 1968), pseudo-isothermal elliptical mass distribution, could also be considered in this context (Kassiola & Kovner 1993). However, strong lensing observables we used are determined by the total mass inside the Einstein radius. Hence, they are not so sensitive to the details of the very central distribution like cusps (besides the extremal cases). Moreover, the Einstein rings of the lenses we used corresponded to less than 10 kpc hence the NFW profile of the dark halo would not likely be manifested.

2019AandA...629L...4A__Arzoumanian_et_al._2011_Instance_1

Herschel imaging observations have shown that filamentary structures are truly ubiquitous in the cold interstellar medium (ISM) of the Milky Way (Molinari et al. 2010), dominate the mass budget of Galactic molecular clouds at high densities (≳104 cm−3) (Schisano et al. 2014; Könyves et al. 2015), and feature a high degree of universality in their properties. In particular, detailed analysis of the radial column density profiles indicates that, at least in the nearby clouds of the Gould Belt, molecular filaments are characterized by a narrow distribution of crest-averaged inner widths with a typical full width at half maximum (FWHM) value Wfil​​ ∼ ​​0.1 pc and a dispersion of less than a factor of ∼2 (Arzoumanian et al. 2011, 2019; Koch & Rosolowsky 2015). Another major result from Herschel (e.g., André et al. 2010; Könyves et al. 2015; Marsh et al. 2016) is that the vast majority (> 75%) of prestellar cores are found in dense “transcritical” or “supercritical” filaments for which the mass per unit length, Mline, is close to or exceeds the critical line mass of nearly isothermal, long cylinders (e.g., Inutsuka & Miyama 1997), M line,crit ​ =  ​ 2 c s 2 /G~16 M ⊙ $ {M_{{\rm{line}},{\rm{crit}}}} = \,2{\mkern 1mu} c_{\rm{s}}^2/G{\mkern 1mu} \sim {\mkern 1mu} 16{\mkern 1mu} {M_ \odot } $ pc−1, where cs​ ∼ ​0.2 km s−1 is the isothermal sound speed for molecular gas at T​ ∼ ​10 K. Moreover, most prestellar cores lie very close to the crests (i.e., within the inner 0.1 pc portion) of their parent filaments (e.g., Könyves et al. 2019; Ladjelate et al. 2019). These findings support a filamentary paradigm in which low-mass star formation occurs in two main steps (André et al. 2014; Inutsuka et al. 2015): (1) multiple large-scale compressions of cold interstellar material in supersonic magneto-hydrodynamic (MHD) flows generate a cobweb of ∼0.1 pc-wide filaments within sheet-like or shell-like molecular gas layers in the ISM and (2) the densest molecular filaments fragment into prestellar cores (and then protostars) by gravitational instability near or above the critical line mass, Mline, crit, corresponding to Σ gas crit ∼ M line , crit / W fil ∼ 160 M ⊙ $ \Sigma_{\mathrm{gas}}^{\mathrm{crit}} \sim M_{\mathrm{line, crit}}/W_{\mathrm{fil}} \sim 160\,M_\odot $ pc−2 in gas surface density (AV ∼ 7.5) or nH2 ∼ 2 × 104 cm−3 in volume density. This paradigm differs from the classical gravo-turbulent picture (Mac Low & Klessen 2004) in that it relies on the anisotropic formation of dense structures (such as shells, filaments, cores) in the cold ISM and the unique properties of filamentary geometry (see Larson 2005).

2016MNRAS.461..248S__Munari_et_al._2013_Instance_4

In Sifón et al. (2013), we used the σ–M200 scaling relation of Evrard et al. (2008) to estimate dynamical masses. As discussed in Section 1, the scaling relation of Evrard et al. (2008) was calibrated from a suite of N-body simulations using DM particles to estimate velocity dispersions. However, the galaxies, from which velocity measurements are made in reality do not sample the same velocity distribution as the DM particles. They feel dynamical friction and some are tidally disrupted, which distorts their velocity distribution and biases their dispersion (e.g. Carlberg 1994; Colín et al. 2000). Recent high-resolution hydrodynamical simulations of ‘zoomed’ cosmological haloes have shown that there is a significant difference between the velocity distributions of DM particles and galaxies themselves; whether galaxies (i.e. overdensities of stars in hydrodynamical simulations) or DM subhaloes are used makes comparatively little difference (Munari et al. 2013). Results from state-of-the art numerical simulations depend on the exact definition of a galaxy and the member selection applied, but the current consensus is that galaxies are biased high (i.e. at a given mass the velocity dispersion of galaxies or subhaloes is larger than that of DM particles) by 5–10 per cent with respect to DM particles (Lau et al. 2010; Munari et al. 2013; Wu et al. 2013), translating into a positive 15–20 per cent bias in dynamical masses when using DM particles. This is illustrated in Fig. 5: DM particles are not significantly impacted by either dynamical friction or baryonic physics; therefore, the scaling relations for DM particles are essentially the same for all simulations. In contrast, DM subhaloes are affected by baryons in such a way that including baryonic feedback (most importantly feedback from active galactic nuclei – AGN, but also from cooling and star formation) makes their velocity dispersions much more similar to those of simulated galaxies. This means we can rely on our analysis of the previous section, based on DM subhaloes, to correct the velocity dispersions measured for ACT clusters, and then estimate dynamical masses using predictions obtained either from galaxies or subhaloes. The difference between the Saro et al. (2013) and Munari et al. (2013) galaxy scaling relations depends on the details of the semi-analytic and hydrodynamical implementations used in Saro et al. (2013) and Munari et al. (2013), respectively. The different cosmologies used in the Millenium simulation (in particular, σ8 = 0.9; Springel et al. 2005) by Saro et al. (2013) and the simulations by (Munari et al. 2013, σ8 = 0.8) may also play a role.

2021AandA...655A.109B__Foreman-Mackey_et_al._2013_Instance_1

When the size of the logarithmic redshift bin is small enough, we can use fluxes in place of luminosities, performing a test on the (non-)evolution with redshift that is completely independent from any assumption on cosmology. Risaliti & Lusso (2019) analysed in detail the choice of the bin size and verified that, as long as Δlog(z)≤0.1, the slope in the relation does not depend on it. Thanks to the statistics available, we chose Δlog(z)=0.06 and we limited our analysis to the redshift bins with more than five objects. We performed the same analysis for bins of size Δlog(z)=0.05 and Δlog(z)=0.07, finding no significant difference (see Fig. 9 for a comparison of the results for different sizes of the redshift bins). For our selection in Γ and the choice of the threshold for the Eddington bias, the division yielded 17 redshift bins. To perform the fitting to the data, we adopted the Python package emcee (Foreman-Mackey et al. 2013), a pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. To check that the results were independent from the employed method, we also performed the analysis using the Linmix package (Kelly 2007), an algorithm that makes use of a Bayesian approach to linear regression and takes into account the errors in both the x and y variable. We performed the fit a first time, then applied a 3σ clipping to the data, repeating this sequence for a total of three times. This yielded no significant difference with respect to the analysis without σ clipping. The results are shown in Figs. 7 and 9, and summarised in Table 3. In Fig. 7, red points indicate when the observations are characterised by an SN 5 in the soft band. Most of them are (X-ray) fainter objects at intermediate redshifts. This confirms that data points that passed our selection criteria, even if with a low SN, follow the relation and are representative of the population of blue quasars.

2019ApJ...887...75T__Stritzinger_et_al._2012_Instance_1

Figure 6 compares the IR SED of SN 2014C with the SEDs of all other interacting SNe for which observations beyond 5 μm are available in the literature. Apart from SN 2014C, only four other strongly interacting SNe IIn have been observed in the mid-IR. Three of these were observed at epochs comparable to the epoch at which SN 2014C was observed in the mid-IR. SN 2005ip was observed at 936 days post-explosion during the cryogenic phase of Spitzer. It was the only interacting SN with an InfraRed Spectrograph (IRS; Houck et al. 2004) mid-IR spectrum from 5 to 12 μm and IRAC photometry at 5.8 and 8 μm (Fox et al. 2010). SN 2006jd was observed at 1638 days, an epoch very similar to that of SN 2014C, with Spitzer/IRAC and WISE (Stritzinger et al. 2012). SN 2010jl was observed at 1279 days with Spitzer/IRAC and SOFIA/FORCAST (Herter et al. 2018) at 11.1 μm, resulting in a deep upper limit after 6400 s of total integration time (Williams & Fox 2015). In all three cases, the photometry and spectra from 1 to 10 μm are well fitted by purely carbonaceous dust models, which we overplot in Figure 6 using dust parameters from the literature. Lastly, SN 1995N was observed at more than 10 yr post-explosion by Spitzer/IRAC and WISE (Van Dyk 2013). Its SED shape differs markedly from those of the other three SNe observed at earlier epochs. The carbonaceous dust model that Van Dyk (2013) fitted to the data is shown in Figure 6. However, we note that the shallow slope from 3 to 10 μm cannot be fitted with a single-temperature dust model, regardless of composition, and would require a range of dust temperatures. Given the late epoch of the observation, one might also consider a nonthermal origin for the IR emission from SN 1995N, as the SED can also be described with a broken power law (Fν ∝ ν−3; also overplotted) with a knee at around 12 μm. In addition to these H-rich interacting SNe, the H-poor interacting SN 2006jc (Ibn) was also observed beyond 5 μm with AKARI (Sakon et al. 2009). Its SED was also best fitted with a two-temperature amorphous carbon dust model. This comparison highlights SN 2014C’s unique SED shape among other interacting SNe for which data are available beyond 5 μm at comparable epochs, showing for the first time an evidence for a silicate dust feature in the IR SED of an interacting SN. It also accentuates the need for observations of interacting SNe at late times, out to decades post-explosion, in the near- to mid-IR, which will be enabled by the upcoming James Webb Space Telescope (JWST).

2019ApJ...887..137S__Vekstein_2017_Instance_2

As mentioned above, the magnetic reconnection is introduced as breaking and reconfiguration of the oppositely directed magnetic field lines in highly conducting plasma. The magnetic field lines collapse near the X-point and form an extended magnetic singularities known as a current sheet. There are two mechanism of the current-sheet formation. The first kind of current-sheet formation is associated with the MHD instabilities (e.g., resistive tearing mode and ideal kink mode) known as spontaneous magnetic reconnection (e.g., White 1984; Baty 2000; Vekstein 2017). The second kind of current sheet can be formed in the MHD stable configuration, where some external perturbations trigger the forced magnetic reconnection (Hahm & Kulsrud 1985). The forced magnetic reconnection may be activated by nonlinear MHD waves, which may be caused by explosive solar activities (e.g., Sakai et al. 1984; Dewar et al. 2013; Beidler et al. 2017). The forced magnetic reconnection may be developed due to boundary perturbations, which induce a surface current in such a way that it opposes the progress of the reconnection (Ishizawa & Tokuda 2000, 2001; Fitzpatrick 2003). The multimode simulation approach has been adopted to investigate the thinning of the current sheet induced by forced magnetic reconnection (Birn et al. 2005). The motion of the photospheric footpoints of the coronal magnetic field may also trigger the forced magnetic reconnection, which may be caused by the explosive solar coronal events (e.g., Vekstein & Jain 1998; Jain et al. 2005; Vekstein 2017). Although there is a remarkable development in the theory of the forced magnetic reconnection, Jess et al. (2010) have suggested that there is no observational evidence of explosive flare or coronal activities triggered by forced magnetic reconnection. They have observed a microflare activity driven by forced magnetic reconnection. The lower solar atmosphere (photosphere & chromosphere) is dominated by cool, partially ionized and collision dominated plasma. Most of the energy releases during the forced magnetic reconnection may be consumed by such plasma systems (e.g., Litvinenko 1999; Chen et al. 2001; Chen & Ding 2006; Litvinenko et al. 2007).

2018ApJ...866...48U__Korngut_et_al._2011_Instance_1

RX J1347.5–1145 is one of the most luminous X-ray galaxy clusters and is located at a redshift of z = 0.451. It was thought to be a relaxed cluster when it was discovered in the ROSAT all sky survey (Schindler et al. 1997). Komatsu et al. (1999) made the first measurements of the Sunyaev–Zel’dovich effect (SZE: Sunyaev & Zeldovich 1972) toward this cluster with the James Clerk Maxwell Telescope at 350 GHz as well as with the 45 m Nobeyama Radio Telescope at 21 and 43 GHz. A higher angular resolution observation of the SZE was performed by Komatsu et al. (2001) using the Nobeyama Bolometer Array and they found a prominent substructure which has no counterpart in the soft X-ray image from ROSAT. The presence of the substructure has been confirmed by Chandra and XMM-Newton (e.g., Allen et al. 2002; Gitti & Schindler 2004) as well as by more recent SZE measurements (Mason et al. 2010; Korngut et al. 2011; Plagge et al. 2013; Adam et al. 2014; Kitayama et al. 2016). Allen et al. (2002) measured the mean temperature of the ICM to be over 10 keV, which is relatively high compared to other typical clusters. Kitayama et al. (2004) and Ota et al. (2008) found a very hot (>20 keV) component of the ICM in this cluster. In addition, the radial profile and spatial distribution of the ICM temperature indicate that the temperature drops to ∼6 keV toward the cluster center so that the cool core is formed (e.g., Allen et al. 2002; Ota et al. 2008; Kreisch et al. 2016). A disturbed morphology is further supported by radio synchrotron observations (e.g., Ferrari et al. 2011) and gravitational lensing maps (e.g., Köhlinger & Schmidt 2014). The total mass of RX J1347.5–1145 within r200 is estimated to be ∼1.5 × 1015 h−1 using weak-lensing analysis, where r200, the radius within which the mean mass density is 200 times the critical density of the universe, is 1.85 h−1 Mpc (Lu et al. 2010) for this galaxy cluster.18 18 They adopted the Hubble constant of 70 km s−1 Mpc−1.

2021MNRAS.505.4289P__Vila-Costas_&_Edmunds_1993_Instance_1

For decades, the metallicity and the above chemical abundance ratios have been estimated in samples of star-forming galaxies (SFGs), by using their emission line ratios to calculate the abundances of the different elements that originate them. Many techniques were developed for such studies, such as the use of the Te-method, photoionization models, or optical calibrations based on strong emission lines (see the review by Maiolino & Mannucci 2019 for a more detailed discussion). By using these techniques, some studies show a correlation between both 12 + log (O/H) and log (N/O) (Vila-Costas & Edmunds 1993; Masegosa, Moles & Campos-Aguilar 1994; Andrews & Martini 2013). On the other hand, it is also important how the different properties of the host galaxies correlate with their chemical abundances. For instance, preliminary studies of the Local Group of galaxies (McClure & van den Bergh 1968; Lequeux et al. 1979) revealed the existence of a relation between the luminosity of an SFG and its metallicity, the so-called luminosity–metallicity relation (the most luminous galaxies have the higher chemical abundances), which has been later studied in further detail (Garnett & Shields 1987; Skillman, Kennicutt & Hodge 1989; Brodie & Huchra 1991; Vilchez 1995; Mateo 1998). Later on it was probed that the luminosity–metallicity relation was in fact the result of a more fundamental mass–metallicity relation (Garnett 2002; Pérez-González et al. 2003; Pilyugin, Vílchez & Contini 2004). This relation has been probed in large sample of SFG both at low-redshift (Contini et al. 2002; Melbourne & Salzer 2002; Lamareille et al. 2004; Tremonti et al. 2004) and at high-redshift (Erb et al. 2006a; Pérez-Montero et al. 2009, 2013; Izotov et al. 2015; Gao et al. 2018; Torrey et al. 2019). In addition, it has been reported that the star-formation rate is also related to gas-phase Z in SFG (Lara-López et al. 2010; Mannucci et al. 2010; Yates, Kauffmann & Guo 2012). Morphological type may also be related with the chemical abundances, as early-type galaxies, despite some of them can present episodes of on-going star-formation (Zhu, Blanton & Moustakas 2010), tend to have on average older stellar populations than late-type galaxies (Kennicutt 1998; Trager et al. 2000; Gebhardt et al. 2003; Schiavon 2007), being thus more chemically evolved.

2018MNRAS.478.4336M__Porter_&_Raychaudhury_2007_Instance_1

Recently filaments, in particular the warm hot intergalactic medium (WHIM) in filaments, have been the pivot of several studies. Using emission in the soft X-ray bands, such studies estimate the WHIM temperature in filaments to be ∼3–8 keV (Eckert et al. 2015; Akamatsu et al. 2017; Parekh et al. 2017; Tanimura et al. 2017). Furthermore, the cosmic microwave background map from the Planck together with the Canada–France–Hawaii Telescope Lensing Survey, as well as the Two-Micron All-Sky Redshift Survey of galaxies suggest that at least half of the missing baryons in the Universe may reside as WHIM in large-scale filaments tracing the dark matter distribution (Van Waerbeke, Hinshaw & Murray 2014; Génova-Santos et al. 2015). Therefore, undeniably it is crucial to characterize the large-scale structure (LSS) of the Universe and comprehend the impact of the cosmic-web on properties of galaxies. The Coma supercluster is one of the richest large-scale structures (Chincarini & Rood 1976) in the nearby Universe comprising two clusters of galaxies, connected by a web of large-scale filaments around $30\,h_{70}^{-1}$ Mpc long (e.g. Fontanelli 1984). The two clusters, Coma (Abell 1656) and Abell 1367, along with the filaments of galaxies dispersed with several small galaxy groups span ∼500 deg2 on the sky (Mahajan et al. 2010). The large-scale filaments in the Coma supercluster have not just been observed by means of the galaxy distribution in the optical wavebands (Gregory & Thompson 1978; Mahajan et al. 2010), but also diffuse emission in the radio continuum (Kim et al. 1989). Studies of clusters and groups at $z$ ∼ 0 have evidently shown that outskirts of groups and clusters (Zabludoff & Mulchaey 1998; Wang, Owen & Ledlow 2004; Rines et al. 2005; Cortese et al. 2007; Tran et al. 2009; Gavazzi et al. 2010; Smith et al. 2010; Sun et al. 2010; Coppin et al. 2011; Mahajan, Raychaudhury & Pimbblet 2012; Verdugo et al. 2012; Mahajan 2013) and filaments of galaxies (Porter & Raychaudhury 2007; Boué et al. 2008; Fadda et al. 2008; Porter et al. 2008; Edwards et al. 2010a; Biviano et al. 2011) are favourable sites for galaxy transformations. Based on a study using optical data from the Sloan Digital Sky Survey (SDSS) data release (DR) 7, Mahajan et al. (2010) found that the star formation (SF)–density relation in the Coma supercluster for the giant galaxies is much weaker than their dwarf counterparts. However, the fraction of star-forming galaxies for both declines to ∼0 at the centre of the clusters (also see Mahajan, Haines & Raychaudhury 2011). Cybulski et al. (2014) further study the star formation in the Coma supercluster by combining a complementary optical data set from SDSS DR 9, with IR data from the Wide-Field Infrared Survey Explorer (Wright et al. 2010) and UV data from the Galaxy Evolution Explorer (GALEX; Martin et al. 2005). Cybulski et al. (2014) corroborated the results of Mahajan et al. (2010, 2011) by probing both obscured and unobscured star formation down to ∼0.02 M⊙ yr−1, in order to quantify the effect of different types of large-scale environments: groups, clusters, filaments, and voids, on quenching SF in galaxies. In the absence of dust in star-forming galaxies, the UV emission is a good tracer of massive (>10 M⊙) star formation. On the other hand, optical emission lines such as H α probe instantaneous star formation over a time-scale of ≲20 Myr (Kennicutt 1998). Assuming that the UV luminosity is not overwhelmed by contribution from the old stellar populations due to the UV upturn such as in massive early-type galaxies (O’Connell 1999), the UV luminosity measures star formation over a time-scale of ∼100 Myr (Kennicutt 1998). Hence, the star formation rate (SFR) estimated from optical emission lines delineates the continuous SF in a galaxy, while the SFR determined from the UV is representative of its recent SF activity. But even though GALEXand its predecessor UV imagers have been used to investigate individual galaxies within clusters and groups (e.g. Hicks & Mushotzky 2005), or galaxy populations therein (e.g. Donas et al. 1990; Donas, Milliard & Laget 1995; Cornett et al. 1998; Boselli et al. 2005b), limited work has been done to analyse the UV properties of galaxies in the large-scale cosmic-web. Since the Coma supercluster is one of the most well studied regions in the nearby Universe, many other authors (e.g. Bernstein et al. 1995; Mobasher et al. 2003; Hammer et al. 2012; Smith et al. 2010; Smith, Lucey & Carter 2012b) have made use of optical and UV data to study the Coma and Abell 1367 clusters and their surroundings. With the advent of large redshift surveys, several studies (Gavazzi et al. 2010; Mahajan et al. 2010, 2011; Gavazzi et al. 2013; Cybulski et al. 2014 ) have also used multiwavelength data at optical, UV, and 21 cm continuum to study the properties of galaxies in the entire supercluster region. In this paper, we make use of similar data sets: UV data derived from GALEX and optical spectroscopic and photometric data from the SDSS for the entire Coma supercluster to further explore the impact of environment on the properties of galaxies. Conventionally, the ‘environment’ of galaxies is quantified as the projected density of galaxies in a fixed 2D or 3D region of the sky (e.g. Dressler 1980). Muldrew et al. (2012) combined 20 published methods of defining environment into two methods: (i) method that uses nearest-neighbours to probe the underlying galaxy density and (ii) fixed aperture methods. Muldrew et al. (2012) found that while the former are better suited for quantifying internal density within massive haloes, the latter fixed-aperture methods are better for probing the large-scale environment. Therefore, in order to characterize the large-scale cosmic-web, a combination of these methodologies is required to quantify the environment on different scales. In this work, we implement their result by making use of two different algorithms to define the large-scale filaments, and high density nodes of the cosmic-web characterized as clusters and groups. We also present a catalogue of all spectroscopically confirmed galaxy members of the Coma supercluster detected in the UV. This paper is organized as follows: in the next section we describe our data sets, followed by definition of environment in Section 3. In Section 4, we analyse the broad-band colours of galaxies as a function of environment, while in Section 5 we study the impact of the large-scale filaments on the properties of galaxies. Finally, we discuss our results in the context of the existing literature in Section 6 and summarize our results in Section 7. Throughout this paper, we use concordance Λ cold dark matter cosmological model with H0 = 70  km s−1 Mpc−1, $\Omega _\Lambda =0.7$, and Ωm = 0.3 to calculate distances and magnitudes. We note that at the redshift of the Coma cluster ($z$ = 0.023) our results are independent of the cosmological model used.

2020AandA...643A..58B__Trujillo_et_al._2001_Instance_1

Adaptive optics (AO, Roddier 1999) is a game changer in the quest for high-angular resolution, especially for ground-based astronomical observations that face the presence of wavefront aberrations introduced by the atmosphere (Roddier 1981). Thanks to AO, the point spread function (PSF) delivered by an optical instrument is much narrower, by a factor up to 50 on the full width at half maximum (FWHM), than the seeing-limited scenario (Roddier 1981). Still, some correction residuals persist and render the AO PSF shape complex to model. Consequently, standard parametric models that reliably reproduce the seeing-limited PSFs, such as a Moffat function (Trujillo et al. 2001; Moffat 1969), become inefficient at describing the AO PSF. Moreover, contrary to seeing-limited observations, AO-corrected images suffer from the anisoplanatism effect (Fried 1982) that strengthens the spatial variations of the PSF on top of instrument defects. Determining the AO PSF is necessary for two major reasons. Firstly, understanding and accurately modeling the PSF morphology is key to diagnosing AO performance. From the PSF, we can identify the major contributors to the AO residual error (Beltramo-Martin et al. 2019; Ferreira et al. 2018; Martin et al. 2017). Secondly, the image delivered by an optical instrument depends on both the science object we want to characterize and the PSF. In order to estimate the interesting astrophysical quantities, one can either use a deconvolution technique (Fétick et al. 2020, 2019a; Benfenati et al. 2016; Flicker & Rigaut 2005; Mugnier et al. 2004; Fusco et al. 2003; Drummond 1998) or include the PSF as part of a model, as is performed in PSF-fitting astrometry/photometry retrieval techniques (Beltramo-Martin et al. 2019; Witzel et al. 2016; Schreiber et al. 2012; Diolaiti et al. 2000; Bertin & Arnouts 1996; Stetson 1987) and galaxy kinematics estimation (Puech et al. 2018; Bouché et al. 2015; Epinat et al. 2010) for instance.

2020AandA...642A...2R__Hyder_&_Lites_1970_Instance_1

Investigation of solar wind outflow velocities is generally performed by the analysis of UV spectrometric observations. The UVCS/SOHO instrument has provided H I Lyα spectral line data over a longer time than a whole solar activity cycle (1996–2012), giving the possibility of studying coronal dynamics in different activity phases. The analysis of UVCS daily Lyα synoptic data, in combination with electron densities derived from the white-light RS observations, has allowed to derive H I outflow speed maps (see e.g. Dolei et al. 2018, 2019). One of the methods based on the synergy between UV and WL observations is the Doppler dimming technique (Hyder & Lites 1970; Noci et al. 1987; Withbroe et al. 1982). It exploits the progressive UV intensity reduction of the coronal resonantly scattered component of the coronal H I Lyα line emission with increasing outflow velocities. The line emission depends on the physical quantities involved in the Lyα resonant scattering process, such as, for instance, the coronal electron density and temperature, and the chromospheric Lyα radiation that excites the coronal H I atoms. The Lyα intensity is also sensitive to the speed of the outflowing plasma from about 50–500 km s−1, that are the typical velocities for neutral hydrogen atoms in the inner corona. Following the approach of Withbroe et al. (1982) and Noci et al. (1987), the intensity of the resonantly scattered Lyα line can be also numerically computed by iteratively tuning the plasma speed value in order to reproduce the observed UV line intensity. The best match between computed and observed Lyα intensity provides an estimate of the solar wind H I outflow velocity. Figure 9 shows an example of 2D outflow velocity map, in the range of heliocentric distances between 1.5 and 4.0 R⊙, obtained via Doppler dimming technique (Dolei et al. 2018). The speed values radially increase with altitude up to about 150–200 km s−1 in the equatorial regions and 400 km s−1 in the polar regions. These values are in agreement with the expected latitudinal distribution of slow and fast solar wind components, corresponding to equatorial regions, and mid-latitude and polar regions, respectively. The methodology put in place throughout this project will be applied later to the data acquired by the Solar Orbiter’s Metis instrument, giving an unprecedented daily picture of the coronal dynamics, see Antonucci et al. (2020) for a detailed description of diagnostic techniques for Metis data.

2021AandA...647A..67B__Cao_2011_Instance_1

Keeping in mind the caveats discussed in Sect. 4.2, a relation between the properties of the jet collimation region and the properties of the accretion disk is suggested by Figs. 5 and 6. According to theoretical models and simulations, both thin disks (e.g., Blandford & Payne 1982; Fendt 2006; Liska et al. 2019) and geometrically-thick hot disks (e.g., Blandford & Begelman 1999; McKinney 2006; Begelman 2012; Mościbrodzka & Falcke 2013; Mościbrodzka et al. 2016) can launch collimated outflows. Due to the higher mass loading and lower speed, the disk-driven jet is expected to dominate the emission in radio galaxies with respect to the de-boosted black hole-launched jet. This is confirmed in observations by the direct imaging of limb-brightened jet structures (e.g., Boccardi et al. 2016a; Mertens et al. 2016; Giovannini et al. 2018) as well as by kinematic studies of radio galaxies, which generally show much lower intrinsic speeds than measured in blazars (Lister et al. 2019). As these properties are observed in high-luminosity and low-luminosity radio galaxies alike, a jet sheath must be produced from disks spanning different accretion regimes. Our results, however, indicate that the disk-driven jet in LERG originates at small disk radii (few RS, as measured in M 87), and indeed the expansion profiles of most of the LERG are well aligned with those of BL Lacs, which are expected to be dominated by the black hole-launched spine (see e.g., Ghisellini et al. 2014). This result is in broad agreement with models of jet launching from ADAFs (e.g., Cao 2011; Yuan & Narayan 2014, and references therein), which predict the formation of a thin and mildly-relativistic outer layer. ADAF models also predict the launch of a nonrelativistic disk-wind component carrying the bulk of the disk mass outflow and spanning a large solid angle. There is no evidence for such a component based on the analyzed VLBI images, at least in the considered frequency regime. The jet profiles in HEG, on the other hand, are all shifted upward, and a back-extrapolation down to the jet base suggests that the jet sheath is launched at larger disk radii. Taking as a reference Cygnus A, which shows the thinnest jet among HEG and for which an initial jet width of ∼200 RS was measured based on GMVA observations (Boccardi et al. 2016b), the present data suggest that thin disks could launch collimated winds with an initial outer radius ≳100 RS. This possible difference in the outer radius of the jet sheath is accompanied by a different extent of the collimation region in HEG and LEG (Fig. 6). Modeling of jet collimation by disk winds, presented by Globus & Levinson (2016), revealed a direct link between the wind outer radius and the collimation radius: for a given wind power, larger wind radii correspond to more extended collimation zones. A sufficiently high ratio (> 0.1) of wind power to jet power is required for this process to be efficient. When this condition is verified in reality, is a matter of debate. In recent simulations presented by Hervet et al. (2017), the diverse kinematic behavior of VLBI knots in blazars of different powers could be well explained by varying this ratio. Except for the least powerful class among BL Lacs (that of the High-frequency peaked BL Lacs, HBLs), whose properties could be reproduced by assuming an absent or very weak wind, ratios larger than 0.3 were suggested for blazars. A question remains concerning the portion of these winds which is actually detected in VLBI observations. When attempting to model the M 87 jet collimation profile, Globus & Levinson (2016) have suggested that the radio emission is produced in the shocked interface between the relativistic jet and the outer wind, which is undetected. Observational constraints on extended disk winds may be provided through other methods. For instance, we note that for all the HERG in our sample (except PKS 1514+00) the detection of ultra-fast outflows was reported based on X-ray observations (Tombesi et al. 2010, 2014; Reynolds et al. 2015). These outflows, whose launching mechanism is unclear, are suggested to be characterized by mildly relativistic speeds, to originate at disk radii of 102 − 104RS (in agreement with our findings), and to carry a significant fraction of the jet kinetic power. Thus collimation via the action of disk winds, where by disk winds we mean a mildly relativistic jet sheath plus possible broader outflows, appears to be a viable mechanism, especially for high-luminosity sources.

2022AandA...659A..85F__Jeffries_et_al._2014_Instance_2

We apply our analysis to five open clusters of ages between ∼10 and 100 Myr that were observed within GES (25 Ori, Gamma Vel, NGC 2547, NGC 2451 B, and NGC 2516). These clusters were selected because they cover the age interval in which the effect of radius inflation could be significant, allowing us to investigate how it evolves with age. The 25 Ori cluster is a group of PMS stars that was discovered by Briceño et al. (2005) in the Orion OB1a association, with an estimated age of 6−13 Myr (Downes et al. 2014; Briceño et al. 2019; Kos et al. 2019; Zari et al. 2019); a dispersed, kinematically distinct population was also found in the region using data from the Gaia Second Data Release (DR2; e.g. Zari et al. 2019). Because only a few stars of the secondary population were observed by GES, only the main cluster is considered here. Gamma Vel (age ∼10−20 Myr, Jeffries et al. 2014, 2017) and NGC 2547 (35 ± 3 Myr, Jeffries & Oliveira 2005) are both located in the Vela OB2 association at a relative separation of ∼2°. Both clusters host two kinematically distinct populations (Jeffries et al. 2014; Sacco et al. 2015); the two Gamma Vel populations (Gamma Vel A and B) are also separated by ∼38 pc along the line of sight (Franciosini et al. 2018). NGC 2451 is a double cluster composed of two open clusters of similar age (30−40 Myr, Randich et al. 2018) located at different distances along the same line of sight (Röser & Bastian 1994; Platais et al. 1996). The GES observations cover the background cluster NGC 2451 B and only a few selected regions of the closer and more dispersed NGC 2451 A. For this reason, we considered only NGC 2451 B here. Finally, NGC 2516 is the oldest cluster in our sample, with an age of ∼100 − 140 Myr (e.g. Lyra et al. 2006; Randich et al. 2018), so that most of its members are already close to or at their main-sequence position. All clusters have solar or slightly subsolar metallicities (Biazzo et al. 2011; Jacobson et al. 2016; Spina et al. 2017).

2018ApJ...854...26L___2015a_Instance_3

The hot emission line of Fe xxi 1354.09 Å and the cool emission line of Si iv 1402.77 Å have been used in many spectroscopic studies to investigate chromospheric evaporation (e.g., Tian et al. 2014, 2015; Li et al. 2015b, 2017a, 2017b; Brosius et al. 2016; Zhang et al. 2016a, 2016b). It is widely accepted that the forbidden line of Fe xxi 1354.09 Å is a hot (log T ∼ 7.05) and broad emission line during solar flares (Doschek et al. 1975; Cheng et al. 1979; Mason et al. 1986; Innes et al. 2003a, 2003b). Meanwhile, IRIS spectroscopic observations show that Fe xxi 1354.09 Å is always blended with a number of cool and narrow emission lines, which are from neutral or singly ionized species. Those blended emission lines can be easily detected at the position of the flare ribbon, including known and unknown emission lines, such as the C i line at 1354.29 Å, the Fe ii lines at 1353.02 Å, 1354.01 Å, and 1354.75 Å, the Si ii lines at 1352.64 Å and 1353.72 Å, and the unidentified lines at 1353.32 Å and 1353.39 Å (e.g., Li et al. 2015a, 2016a; Polito et al. 2015, 2016; Tian et al. 2015, 2016; Young et al. 2015; Tian 2017). In order to extract the hot line of Fe xxi 1354.09 Å and the cool line of C i 1354.29 Å (log T ∼ 4.0; Huang et al. 2014), we apply a multi-Gaussian function superimposed on a linear background to fit the IRIS spectrum at the “O i” window (e.g., Li et al. 2015a, 2016a), which has been pre-processed (i.e., IRIS spectral image deformation, bad pixel despiking and wavelength calibration) with the standard routines in Solar Soft Ware (SSW; Freeland et al. 2000). In short, the line positions and widths of these blended emission lines are fixed or constrained, and their peak intensities are tied to isolated emission lines from similar species. More details can be found in our previous papers (Li et al. 2015a, 2016a). On the other hand, the cool line of Si iv 1402.77 Å (log T ∼ 4.8) at the “Si iv” window is relatively isolated, and it can be well fitted with a single-Gaussian function superimposed on a linear background (Li et al. 2014, 2017a). Using the relatively strong neutral lines (i.e., “O i” 1355.60 Å and “S i” 1401.51 Å), we also perform an absolute wavelength calibration for the spectra at the “O i” and “Si iv” windows, respectively (Tian et al. 2015; Tian 2017). Finally, the Doppler velocities of Fe xxi 1354.09 Å, C i 1354.29 Å, and Si iv 1402.77 Å are determined by fitting line centers removed from their rest wavelengths (Cheng & Ding 2016b; Guo et al. 2017; Li et al. 2017a). As the hot Fe xxi line is absent in the non-flaring spectrum, the rest wavelength for the Fe xxi line (i.e., 1354.09 Å) is determined by averaging the line centers of the Fe XXI profiles which were used in the previous IRIS observations (Brosius & Daw 2015; Polito et al. 2015, 2016; Sadykov et al. 2015; Tian et al. 2015; Young et al. 2015; Brosius et al. 2016; Lee et al. 2017), while the rest wavelengths for the C i and Si iv lines, i.e., 1354.29 Å and 1402.77 Å, respectively, are determined from their quiet-Sun spectra (Li et al. 2014, 2015a).

2020AandA...642A..24W__Willingale_&_Mészáros_2017_Instance_1

Selected as part of ESA’s Cosmic Vision programme, Athena will study the evolution of large scale structure through the detection of WHIM filaments to trace the missing baryons in the local universe. Athena aims to measure the local cosmological baryon density in the WHIM to better than 10% and to constrain structure formation models in the low-density regime by measuring the redshift distribution and the physical parameters of 200 filaments against bright background sources. To achieve this, Athena will detect 200 filaments in the WHIM through absorption, 100 towards active galactic nuclei (AGN), and 100 towards bright gamma-ray burst (GRB) afterglows, up to redshifts of z = 1 (Kaastra et al. 2013). The WHIM can be detected by observing the absorption of highly ionised elements such as C, N, O, Ne, and possibly Fe (e.g. O VII, O VIII, Ne IX, Fe XVII). The strongest lines expected correspond to the H-like and He-like oxygen ions of the O VII 1s−2p X-ray resonance line (574 eV) and the unresolved O VIII 1s−2p X-ray doublet (653.5 eV, 653.7 eV). A bright X-ray background source such as an AGN (e.g. Padovani et al. 2017) or a GRB (e.g. Willingale & Mészáros 2017) can be used to detect the WHIM by producing absorption features in its energy spectra (Perna & Loeb 1998; Hellsten et al. 1998; Fiore et al. 2000). These sources provide a high probability of detection for the WHIM because they are sufficiently bright and distant to obtain a large statistical sample of lines. Bright AGN are common but are typically nearby, having an average redshift of z ≈ 0.8 for flat-spectrum radio quasars (FRSQ) and z ≈ 0.3 for BL-Lacs (Ackermann et al. 2011) and so, they probe relatively short lines of sight. Gamma-ray bursts occur at an average redshift of z ≈ 2 (Evans et al. 2009) and have been detected out to redshifts of 9.4 (Cucchiara et al. 2011). This allows for the probing of long lines of sight and can potentially provide multi-filament detections in a single observation. In addition, GRBs occur at an approximate rate of 1 GRB per day, with Fermi-GBM detecting ≈250 per year with a ≈70% sky coverage (Bhat et al. 2016). Both sources provide similar fluences in the 0.3−10 keV energy range, but GRBs can provide this fluence in much shorter integration times. The challenge associated with the use of GRBs as background sources is their transient nature, emitting a considerable percentage of the soft X-ray photons within the first hour of their afterglow phase. Therefore, an instrument capable of having a high efficiency to react on target of opportunity (ToO) events is required to probe the WHIM with GRBs. In spite of this, GRBs should allow Athena to perform its science mission of tracing the missing baryons in GRB afterglow spectra throughout its four-year mission lifetime.

2021ApJ...910...78Z__Condon_et_al._2017_Instance_1

The multiwavelength spectral data of 13 SNRs with hard γ-ray spectra. The γ-ray spectra are fitted with a hadronic model with the normalization of the individual spectrum as free parameters. The model assumes that protons have a single power-law energy distribution with an exponential high-energy cutoff. Note that the TeV spectra of G78.2+2.1 (HAWC) and N132D (HESS) cut off at relatively lower energies, and the soft spectral component of GeV of HESS 1912+101 may be from other contributors and are not considered in SED fitting. The best-fit model parameters are indicated in the figure. References for the observational data are as follows: RX J0852.0−4622: radio (Duncan & Green 2000), GeV (Tanaka et al. 2011), X-ray (Aharonian et al. 2007), TeV (H.E.S.S. Collaboration et al. 2018c); RX J1713.7−3946: radio (Lazendic et al. 2004), X-ray (Tanaka et al. 2008), GeV and TeV (H.E.S.S. Collaboration et al. 2018a); HESS J1731−347: radio (Tian et al. 2008), GeV (Condon et al. 2017; Guo et al. 2018), X-ray (Doroshenko et al. 2017), TeV (H.E.S.S. Collaboration et al. 2011); RCW 86: radio (Clark et al. 1975; Lemoine-Goumard et al. 2012), X-ray (Lemoine-Goumard et al. 2012), GeV (Ajello et al. 2016), TeV (H.E.S.S. Collaboration et al. 2018d); SN 1006: radio Dyer et al. 2009, X-ray (Bamba et al. 2008), GeV (Condon et al. 2017), TeV (Acero et al. 2010); G150.3+4.5: radio (Gerbrandt et al. 2014), X-ray and GeV (Devin et al. 2020); G296.5 + 10.0: radio (Milne & Haynes 1994), GeV (this work), HESS J1534−571: radio (Maxted et al. 2018), GeV (Araya 2017), X-ray and TeV (H.E.S.S. Collaboration et al. 2018b); RCW 103: radio (Dickel et al. 1996), GeV (Xing et al. 2014); G78.2+2.1: radio (Wendker et al. 1991; Zhang et al. 1997; Kothes et al. 2006; Gao et al. 2011), X-ray (Leahy et al. 2013), GeV (Abeysekara et al. 2018) and TeV (Fleischhack 2019); G279.0+1.1: radio (Woermann & Jonas 1988; Duncan et al. 1995), GeV Araya (2020); N132D: radio (Dickel & Milne 1995), X-ray (Hughes et al. 1998; Bamba et al. 2018), GeV (Y. L. Xin et al. 2020, in preparation), and TeV (H.E.S.S. Collaboration et al. 2015).

2015ApJ...803...17Z__Campbell_&_Lattanzio_2008_Instance_1

The metallicity calculated for HD 212869 is higher than that estimated prior to using high-resolution spectra. The iron abundance is found to be [Fe/H] = 0.2 dex on the basis of ionized lines that are almost free from NLTE effects. The carbon abundance was found to be high in the atmosphere of HD 112869, = 8.3 ± 0.1 dex. The nitrogen abundance is = 6.55 ± 0.2 dex. With the obtained abundances [C/Fe] = +2.2 dex and [C/N] = +1.15, HD 112869 occupies the region of CEMP-s stars on the plots [C/Fe] versus [Fe/H] and [C/N] versus [Fe/H] (see Figures 5 and 6 in Campbell & Lattanzio 2008). However, the s-process elements Sr, Y, and Ba are not enhanced significantly, thus confirming by definition (Beers & Christlieb 2005) the CEMP-no status of HD 112869. However, the Nd abundance seems to be enhanced relative to iron, and a similar overabundance was recognized for lanthanum and samarium. From inspection of the Eu ii line at 6645.127 Å, the upper limit was set for the r-process element europium, [Eu/Fe] ≲ +0.8 dex. According to calculations carried out by Bisterzo et al. (2011), the abundances of three s-peaks are strongly dependent on the choice of the -pocket, as well as on the initial mass and the metallicity. [ls/Fe], [hs/Fe] and [Pb/Fe] do not follow a linear behavior with decreasing metallicity and can cover a large range of values. For the low-mass models the enhancement of the first s-process peak is low or absent. A spectrum of very high resolution is needed to estimate the abundances for a large number of the second s-process peaks and to recognize an overabundance of the third s-process peak. With the adopted oxgen abundance, [O/Fe] = +0.8 dex, the carbon-to-oxygen ratio was found to be very high for HD 112869, C/O 12.6. The isotopic lines of C2 and CN are too week to be detected in the crowded spectrum, and the lower limit of isotopic ratio was found to be extremely high, 1500. A large isotopic ratio is not typical of CEMP stars. However, for low-mass AGB stars the CN processing is not expected after the second dredge-up and a total amount of dredged-up during the AGB phase leads to a high [C/N] ratio and a high ratio observed for HD 112869. On the contrary, intermediate-mass AGB stars with a hot bottom burning should have low both [C/N] and ratios.

2021AandA...646A..96C__Brusa_et_al._2018_Instance_1

AGN-driven outflows. Another possible effect of the AGN activity on the molecular gas is through outflows. This possibility is supported by observations of individual objects: For example, Carniani et al. (2017), Brusa et al. (2018) and Loiacono et al. (2019) find low gas fractions in powerful AGN at cosmic noon hosting high-velocity molecular and ionized outflows (but see also Herrera-Camus et al. 2019). AGN feedback in action in these targets could be depleting the molecular gas reservoir (Brusa et al. 2015). Förster Schreiber et al. (2019), studying outflows in a large sample of 0.6  z  2.7 galaxies through integral field spectroscopy of the Hα emission line, find that incidence, strength, and velocity of AGN-driven winds are strongly correlated with the stellar mass. In particular, they find that high-velocity (∼1000–2000 km s−1) AGN-driven outflows are commonly detected at masses above log(M*/M⊙) = 10.7, and present in up to 75% of the population for log(M*/M⊙) > 11.2. Interestingly, above this stellar-mass threshold we find a significant CO luminosity deficit in our AGN sample with respect to inactive galaxies (Fig. 3, bottom). Moreover, our AGN show on average gas fractions 0.57 dex (by using uniform assumptions, Sect. 4) lower than inactive galaxies at the 2.2σ level. Quantitatively, this translates into Mgas, mol/M* ≈ 0.3 for AGN (0.16 if we use r31 = 0.92; Kirkpatrick et al. 2019) and ≈1 in inactive galaxies. This representative value for our AGN is in line but not as low as previous work targeting extremely powerful sources (e.g., Mgas, mol/M*  0.05 in Brusa et al. 2018). Our team is performing a systematic investigation of ionized gas outflows with SINFONI as part of the SUPER survey, and 11 targets of our ALMA sample have complementary good quality SINFONI data (Kakkad et al. 2020; Perna et al., in prep.). For some of them we measured [O III] line widths larger than 600 km s−1, interpreted as a clear signature of the presence of an AGN-driven outflow in these objects (Kakkad et al. 2020). A detailed comparison between outflow and CO properties for these targets will be presented in a future work. Distinguishing among the scenarios described above is challenging with the current dataset. AGN feedback could proceed in different ways and different mechanisms likely overlap in shaping the properties of the molecular gas reservoir. For example, AGN radiation could both heat and/or dissociate CO molecules. In this case, AGN would produce a feedback mechanism that does not require outflows but would potentially work toward inhibiting further star formation. As for AGN-driven outflows, they could impact the gas content by ejecting material out of the galaxy (e.g., Travascio et al. 2020), or they could produce CO heating or dissociation due to shocks. Additionally, numerical simulations predict that AGN-driven outflows may heat via shocks a significant quantity of the gas in the ISM, reaching the high temperatures required for the excitation of high-J CO transitions (Costa et al. 2018). To reach a deeper understanding of the impact of AGN on the molecular gas reservoir, also on longer timescales, predictions from simulations providing the spatial scales and effects of AGN activity on CO properties as a function of cosmic time are needed.

2022MNRAS.511.1121M__Reig_&_Nespoli_2013_Instance_3

Critical luminosity (Lcrit) is the luminosity above which a state transition from subcritical to supercritical takes place. The subcritical state (LX Lcrit) is known to be the low luminosity state whereas the supercritical state is high luminosity state (LX > Lcrit) (Becker et al. 2012). The critical luminosity is crucial to determine whether the radiation pressure of the emitting plasma is capable of decelerating the accretion flow (Basko & Sunyaev 1976; Becker et al. 2012). The luminosity during the 2020 giant outburst reached a record high, which was significantly higher than the critical luminosity (Reig & Nespoli 2013). The source entered a supercritical regime from a subcritical regime during the outburst. In the supercritical regime, radiation pressure is high enough to stop the accreting matter at a distance above the neutron star, forming a radiation-dominated shock (Basko & Sunyaev 1976; Becker et al. 2012). For the subcritical regime, accreted material reaches the neutron star surface through nuclear collisions with atmospheric protons or through Coulomb collision with thermal electrons (Harding 1994). These accretion regimes can also be probed by noting changes in the cyclotron line energies, pulse profiles, and changes in the spectral shape (Parmar, White, & Stella 1989; Reig & Nespoli 2013). During the transition from the subcritical to the supercritical regime, sources show two different branches in their hardness–intensity diagram (HID) which are known as horizontal branch (HB) and diagonal branch (DB) (Reig & Nespoli 2013). The HB implies the low-luminosity state of the source, which is represented by spectral changes and high X-ray variability. The DB corresponds to the high-luminosity state that appears when the X-ray luminosity is above the critical limit. The classification HB and DB depends on HID patterns that the source follows. The HB pattern is generally observed in the subcritical regime and the DB pattern is observed in the supercritical regime (Reig & Nespoli 2013).

2019ApJ...885...50W__Falgarone_et_al._2009_Instance_1

The physical conditions within giant molecular clouds (GMCs) establish the initial conditions for star formation, thus understanding the factors that determine molecular cloud properties is of major interest. A correlation between the size and line width, of the form σv ∝ Rα with α ≈ 0.5, has long been noted in samples of nearby molecular clouds (Larson 1981; Solomon et al. 1987, hereafter S87). This correlation, hereafter referred to as the R–σv relation, is usually interpreted as the result of turbulent motions in the interstellar medium on all scales (Mac Low & Klessen 2004; Falgarone et al. 2009). It closely resembles the turbulent cascade with a power-law slope falling between the Kolmogorov (1941) and Burgers (1939) values for incompressible and highly supersonic turbulence, respectively (see also Falgarone et al. 1994; Brunt & Heyer 2002; Federrath 2013; Kritsuk et al. 2013). At the same time, a study of 13CO emission in the Boston University–FCRAO Galactic Ring Survey (GRS) by Heyer et al. (2009) showed that the normalization of the relation, , exhibits a linear correlation with the mass surface density, Σ = M/πR2, across more than an order of magnitude in Σ. The data are consistent with a state of virial balance between gravity and turbulent motions, except that the observed normalization, v0, is about a factor of 2 too large. Subsequent work by Field et al. (2011) has suggested that the larger than expected v0 may result from external pressure confinement, although to a lesser extent than has been inferred for clouds in the outer Galaxy (Heyer et al. 2001) or near the Galactic Center (Oka et al. 2001; Shetty et al. 2012). On the other hand, Ballesteros-Paredes et al. (2011) have interpreted the Heyer et al. (2009) result in terms of gravitational collapse near freefall, which differs from the virial equilibrium prediction by a factor of in v0 and is thus roughly consistent with the GRS data. A third possibility is that errors in the measured or inferred cloud properties create the appearance of excess kinetic energy when in fact clouds are close to being virialized.

2018ApJ...856...94Z__Bieber_et_al._1991_Instance_1

Figures 8 and 9 show the effects of solar activity on the CR parallel λ∥ (blue line), perpendicular λ⊥ (red line), and radial mean free path λrr (gray line) for a proton with rigidity 445 MV (corresponding to a 100 MeV proton) for the inwardly and outwardly directed IMF, respectively. As described in Zank et al. (1998), the parallel mean free path (mfp) based on standard QLT and assuming magnetostatic turbulence is approximated by 13 where , , and . RL is the particle Larmor radius, P is the particle rigidity, and B0 is the mean magnetic field strength. The analytic form of the perpendicular mfp based on NLGC theory is given by (Zank et al. 2004; Shalchi et al. 2010) 14 where a2 = 1/3 is a factor related to the gyrocenter velocity. is a constant such that ν = 5/6 yields a Kolmogorov (1941) spectrum. Note that Equation (14) was derived under the assumption of a specific form of 2D wave spectrum, which is a constant at large turbulence scales. It means that the 2D turbulence spectrum is independent of wavenumber in the energy range in Equation (14). Observation of magnetic fluctuations in the SW indicates that omnidirectional power spectra approach a k−1 wavenumber dependence and that low-frequency turbulence exhibits some sunspot cycle variability (Bieber et al. 1991). Based on this, Engelbrecht & Burger (2015) derived the perpendicular mfp by specifying the energy range spectral index of 2D turbulence power spectra as −1. A more general form of the 2D power spectrum with an energy range spectral index q was proposed by Shalchi et al. (2010). They show that the spectral index has a strong influence on the perpendicular diffusion coefficient. In their model, negative values of q correspond to a decreasing spectrum in the energy range, q = 0 corresponds to the constant spectrum we use here, and positive values of q correspond to an increasing spectrum. Matthaeus et al. (2007) presented a similar spectrum in different regimes: energy range, inertial range, and intermediate regime where the spectrum is proportional to k−1 to coincide with observations (Bieber et al. 1991; Goldstein & Roberts 1999). However, Shalchi (2013) argues that a spectrum that behaves like k−1 does not provide a different perpendicular diffusion coefficient (see also Shalchi et al. 2010), since the field lines for such length scales behave superdiffusively as in the inertial range (Shalchi & Kourakis 2007). In view of this uncertainty, we do not take into account a more elaborate spectrum in the present paper. The behavior of the 2D wave spectrum in the energy range, which may also be correlated with the sunspot cycle, is an important factor in deriving the CR perpendicular mfp. A general form (e.g., Shalchi et al. 2010; Shalchi 2013) should be employed in future studies of CR diffusion.

2020ApJ...892L..10Y__Macchi_2013_Instance_1

In this section, we consider the plasma properties under the propagation of strong waves. In strong waves, the motion of electrons in the plasma becomes relativistic. However, different from free electrons that have a relativistic drift velocity in the direction of the incident electromagnetic wave (see Section 3.1), in plasma the space-charge potential is important in preventing the drift of electrons (Waltz & Manley 1978). For nonrelativistic electrons in plasma, if the wave duration τ is much larger than c/ωp, where is the plasma frequency, the drift velocity would be close to zero (Waltz & Manley 1978; Sprangle et al. 1990b). In this case, electrons in plasma under a strong wave would have a typical Lorentz factor ( ) similar to that (γ) in the laboratory frame, so that is satisfied. Due to the relativistic and magnetic force effects, the propagation and dispersion properties of an electromagnetic wave depend on its amplitude. For a circular polarized wave, the dispersion relation in the laboratory frame is given by (e.g., Gibbon 2005; Macchi 2013; Macchi et al. 2013; see the Appendix) 11 The dispersion relation of strong electromagnetic waves is altered due to the effective electron mass increased by the relativistic effect (e.g., Sarachik & Schappert 1970; Gibbon 2005; Macchi 2013). One can define the effective plasma frequency as 12 so that the wave can propagate in the region where . With respect to the nonrelativistic linear case, this is known as relativistically self-induced transparency. We note that since the dispersion depends on the electromagnetic field amplitude in the nonlinear case, the dispersion relation must be taken with care. The propagation of a pulse will be affected by the complicated effects of nonlinear propagation and dispersion, and finally the spatial and temporal shape of the pulse itself would also be modified. In particular, for linear polarization, the relativistic factor γ is not a constant (see Section 3.1). The propagation of the linearly polarized wave with a relativistic amplitude would lead to generation of the higher-order harmonics. Sprangle et al. (1990b) proved that the propagation of the first harmonic component, i.e., of the “main” wave, is still reasonably described by Equation (11) with . Thus, we will directly adopt Equation (11) in the following discussion.

2016ApJ...822...37S__Freeman_et_al._2001_Instance_1

Each pulsar system in this analysis was observed with XMM-Newton (Jansen et al. 2001), using the European Photon Imaging Camera (EPIC) with the pn detector in full frame mode with thin filters (the data from the MOS and RGS detectors had insufficient counts for analysis). PSR J0337 was observed on 2013 August 1 (observation number 0722920101) for 16.2 ks. An X-ray source was detected 16 away from the radio position of Ransom et al. (2014), consistent with the 2″ astrometric precision of XMM;12 12 See xmm.vilspa.esa.es/docs/documents/CAL-TN-0018.pdf. we show an image of the detection in Figure 1. We measured 164 ± 13 background-subtracted counts between 0.2 and 2.0 keV, as determined using calc_data_sum in Sherpa (Freeman et al. 2001; Doe et al. 2007) and uncertainty given by a Poisson distribution.13 13 Note that our count-rate for PSR J0337 is below the 2σ upper limit from Prinz & Becker (2015), who analyzed the same data set. Nonetheless, we are confident in our detection (Figure 1) and do not know the reason for the discrepancy. The chance coincidence probability, given the number of sources in the field with similar or higher count rates, is approximately 8 × 10−5. PSR J0636 was observed on 2013 October 13 (observation number 0722920201) for 15.0 ks, and we found an X-ray source within 03 of the radio position of Stovall et al. (2014); see Figure 1. The chance coincidence probability for PSR J0636 is also approximately 8 × 10−5. We measured 170 ± 13 counts between 0.2 and 2.0 keV. Finally, PSR J0645 was observed on 2014 March 29 (observation number 0722920301) for 34.9 ks, but removing a flare from the data reduced the effective observation length to 23 ks. No source was found by the XMM pipeline near the radio position of Stovall et al. (2014, see Figure 1), and we measured only 18 ± 9 source counts between 0.2 and 2.0 keV. The time resolution of 73.4 ms was too coarse to detect pulsations at the rotational periods of the pulsars (2.73, 2.87, and 8.85 ms; Ransom et al. 2014; Stovall et al. 2014), but the observed flux can guide future searches for pulsed X-rays. We reprocessed the data using SAS v13.0.1, specifically epchain. Using HEAsoft v6.14 and CIAO v4.6, and some custom scripts, we extracted the source counts from within a radius of 25″, and background counts from an annular region with radii of 50″ and 125″, restricted to the same CCD chip with other sources removed. We limited the data to events with PATTERN ≤ 4 (singles and doubles), but also experimented with using PATTERN ≤ 12 (singles, doubles, and triples). We found that the change in the results when including triple events was negligible. Because of the high background rate at low energies and the expected softness of the source spectra, we limited our analysis to energies between 0.2 and 2.0 keV. We grouped the counts such that each energy bin had at least 15 events in it and subtracted the background from the source.

2022AandA...667A..69S__Hut_1985_Instance_1

Finally, it is worth noting that two-body relaxation and tidal interactions affect the IMF in different ways. The former causes mass segregation with higher-mass stars moving inward and lower-mass stars outward, which enables the lower-mass stars to evaporate from the cluster (see also, e.g., Chandrasekhar 1942; Spitzer 1969; Binney & Tremaine 2008). The cluster then forms a dense core of high-mass stars and once it starts to collapse, binaries form in the centre. These perturb their neighbours, and we begin to observe massive stars escaping from the mass segregated core (see also Hills 1975; Hut 1985). On the other hand, if the relaxation timescales are long, the cluster does not segregate as quickly and the tidal harassment is the dominant reason for mass-loss. Stars are peeled-off from the outer regions independent of their masses. Consequently, the relative contributions of these two processes determine the mass distribution of the escapers. This is shown in Fig. 5, where the mass distribution of escaping stars divided by the original IMF in models R07_Sal13 and R07_Sal13_Nb. When the environment is present, the tidal shocks enhance fraction of escaping stars at intermediate masses. Instead, the loss of massive stars, similar in the two models, can be linked to the relaxation process ongoing in the core that is composed almost exclusively of massive stars and binaries at later times. In our model, the percentage of mass loss is small so this does not significantly affect the mass function of the remaining members. However, the same process happens in protoclusters still embedded in the parent cloud. Their gas density is orders of magnitude higher than our n = 10 cm−3, and structures are closer to the cluster (Kruijssen et al. 2012; Kruijssen 2012). At the same time, the IMF of massive protoclusters is much wider, since massive stars are still on the main sequence, so that the evolutionary timescale is also reduced (Allison et al. 2009; Yu et al. 2011). In the end, the effects we described could indeed play a crucial role when inferring the IMF of young clusters.