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SAO/NASA Astrophysics Data System 13

2020ApJ...900..143S__Potgieter_et_al._2014_Instance_1

It is well known that there exists an anticorrelation between the cyclic variations of GCR intensity and SSN (Forbush 1954), and the time lag in this relationship has also been reported by Forbush (1958). The time lag in solar modulation of GCRs should be closely related to the transport of solar wind plasma with its embedded HMF through the modulation region. This effect has been considered in numerical models of GCR modulation to study the long-term modulation mechanism (e.g., Le Roux & Potgieter 1990; Ferreira & Potgieter 2004; Bobik et al. 2012; Potgieter et al. 2014; Qin & Shen 2017). Moreover, the delay time in odd solar cycles was found to be longer than that in even ones (e.g., Nagashima & Morishita 1980; Mavromichalaki & Petropoulos 1984; Usoskin et al. 1998; Mavromichalaki et al. 2007; Ross & Chaplin 2019). Drift theory has predicted that positively charged particles mainly drift inward through the polar region of the heliosphere during the A > 0 solar magnetic cycle, while they predominantly participate in the heliosphere along the wavy heliospheric current sheet when A 0 (Jokipii et al. 1977; Jokipii & Thomas 1981). Thus, positively charged particles will take a longer time to reach Earth when A 0, and such modulation mechanism may cause a longer delay time in odd cycles (Van Allen 2000; Cliver & Ling 2001). In addition, the time lag varies with the energy/rigidity of the particle, which is easy to understand: the higher the particle’s energy is, the shorter its propagation time in the modulation region. Such phenomenon is implied from several theoretical (O’Gallagher 1975; Strauss et al. 2011) and observational (Burger & Swanenburg 1973; Nymmik & Suslov 1995) studies. Meanwhile, the difference in the energy dependence of the time lag in odd and even solar cycles was also shown by Nymmik & Suslov (1995). However, in the correlation studies of Ross & Chaplin (2019), an obvious rigidity dependence on the delay time between SSN and GCR intensity recorded by NMs was not found. Thus, we focus on the GCR data from spacecraft rather than those from NMs in this work.

2018AandA...619A..59S__Sander_et_al._2015_Instance_1

Information about fundamental parameters of stars – like their mass, luminosity, surface temperature and chemical composition – comes primarily from matching observations to synthetic spectra computed using models of stellar atmospheres. For massive stars with hot surfaces, scattering and absorption in spectral lines further transfer momentum from the star’s intense radiation field to the plasma, and so provides a force that overcomes gravity and drives a wind-outflow directly from the stellar surface (Castor et al. 1975). These starlight-powered winds are very strong and fast, and can dramatically affect the star’s atmospheric structure (review by Puls et al. 2008) as well as the evolution of its mass and luminosity, chemical surface abundances, rotational velocity, and nuclear burning life-times (review by Smith 2014). Atmospheric models of such hot, massive stars must thus generally be constructed using a unified, or global, approach, wherein the basic structural equations for the quasi-static photosphere and the outflowing stellar wind are solved simultaneously (Gabler et al. 1989). In addition, the expanding atmospheres of these stars are characterized by their large departures from local thermodynamic equilibrium (LTE), meaning the full number density rate equations (typically reduced to statistical equilibrium, and often simply called non-LTE or NLTE) must be solved to obtain the atmospheric radiation field and the excitation and ionization balance. As such, quite intricate numerical solution techniques are normally required to compute synthetic observables, like spectral lines and energy distributions, for these objects (for details, see book by Hubeny & Mihalas 2014). Over the past decades, much effort has been devoted toward constructing such global NLTE, steady-state model atmospheres of hot stars with winds; several numerical computer codes meanwhile exist on the market, for example CMFGEN (Hillier & Miller 1998), POWR (Gräfener et al. 2002; Sander et al. 2015), PHOENIX (Hauschildt 1992), WM-BASIC (Pauldrach et al. 2001), and the subject of this paper, FASTWIND (Santolaya-Rey et al. 1997; Puls et al. 2005; Rivero González et al. 2011; Carneiro et al. 2016). FASTWIND is routinely applied for both photospheric and wind analyses of hot stars, and used for detailed studies of individual objects as well as in large spectroscopic surveys (like within the recent VLT-FLAMES survey of massive stars in the Tarantula giant star-forming region in the Large Magellanic Cloud, Evans et al. 2011). A critical component of all these codes regards their practical treatment of the stellar wind; traditionally this has been to assume a parametrized steady-state and smooth outflow, without any clumps or shocks. However, it has been known for quite many years now, that these line-radiation driven winds are in fact inhomogeneous and highly structured on small spatial scales (see overviews in Puls et al. 2008, 2015; Hamann et al. 2008; Sundqvist et al. 2012b). Such wind clumping arises naturally from the strong line-deshadowing instability, the LDI, a fundamental and inherent property of line driving (e.g., Owocki & Rybicki 1984, 1985). Radiation-hydrodynamic, time-dependent wind models (Owocki et al. 1988; Feldmeier et al. 1997; Owocki & Puls 1999; Dessart & Owocki 2003; Sundqvist & Owocki 2013, 2015; Sundqvist et al. 2018) following the non-linear evolution of this LDI show a characteristic two-component-like structure consisting of spatially small and dense clumps separated by large regions of very rarified material, accompanied by strong thermal shocks and a highly non-monotonic velocity field. Such clumpy winds then affect both the atmospheric structure and the radiative transfer needed to derive synthetic observables; as just one example of this, neglecting clumping typically leads to observationally inferred mass-loss rates that might differ by more than an order of magnitude for the same star, depending on which spectral diagnostic is used to estimate this mass loss (Fullerton et al. 2006). Global model atmospheres nowadays normally account for such wind inhomogeneities by simply assuming a two-component medium consisting of overdense, optically thin clumps of a certain volume filling factor, following a smooth, parametrized velocity law, and an inter-clump medium that is effectively void (e.g., Hillier 1991; Puls et al. 2006). However, if clumps become optically thick, it leads to an additional leakage of light – not accounted for in the filling factor approach – through porous channels in between the clumps. Such porosity can occur either spatially (e.g., Feldmeier et al. 2003; Owocki et al. 2004; Sundqvist et al. 2012a), or for spectral lines in velocity-space due to Doppler shifts in the rapidly accelerating wind (sometimes thus called velocity-porosity, or “vorosity”, Owocki 2008). Regarding spatial porosity, several studies over the past years have focused on examining potential effects on the bound-free absorption of X-ray photons by the bulk wind (e.g., Oskinova et al. 2006; Owocki & Cohen 2006; Sundqvist et al. 2012a; Leutenegger et al. 2013; Hervé et al. 2013). Regarding velocity-space porosity, similar studies (Oskinova et al. 2007; Hillier 2008; Sundqvist et al. 2010, 2011, 2014; Šurlan et al. 2012, 2013) have shown that clumps indeed very easily become optically thick in especially the strong UV wind-lines of hot stars (the so-called P-Cygni lines), and that the associated additional leakage of line-photons leads to weaker line profiles than predicted by smooth or volume filling factor models1. But constructing realistic, multi-dimensional ab-initio radiation-hydrodynamic wind simulations that account naturally for (time-dependent) spatial and velocity-field porosity is an extremely challenging and time-consuming task (Sundqvist et al. 2018). Thus there has also been a big need for developing simplified, parameterized models that can be more routinely applied to diagnostic work on samples of hot stars with winds. Building on their prior studies Sundqvist et al. (2010; 2011; 2012a; 2014, hereafter SPO14) developed and benchmarked such a method, using effective quantities to simulate the reduction in opacity associated with optically thick clumps. In contrast to some other models mentioned above, this “effective opacity” approach has the great advantage that it can be quite readily implemented into the already existing (time-independent) global NLTE atmosphere models discussed above.

2022MNRAS.516..167B__Steinborn_et_al._2015_Instance_1

Otherwise (i.e. if there is insufficient mass left in the sub-grid gas reservoir), the mass deficit $m_\mathrm{BH} + \Delta m - m_\mathrm{BH}^\mathrm{dynamical}$ (the last term denotes the dynamical mass of the SMBH) is drawn from the surrounding gas particles. In eagle, this was done by stochastically swallowing individual gas neighbours. This is not an ideal approach: the momentum imparted on the SMBH from the swallowed gas particle may artificially dislodge it from its position, particularly without instantaneous repositioning (as also discussed by Steinborn et al. 2015). The mass of a gas particle is also typically much greater than Δm, so that the dynamical mass of SMBH particles remains systematically above its sub-grid mass. Both issues become more severe when individual gas particles have been enriched to masses well above their initial value due to stellar outflows, which is particularly common in massive, gas-poor galaxies. Instead of swallowing entire particles, we therefore transfer a (typically very) small fraction of mass from all gas neighbours to the SMBH simultaneously, with the mass δmi ‘nibbled’ from each neighbour i weighted in analogy to their contribution to the gas density at the location of the SMBH, (4)$$\begin{eqnarray} \delta m_i = (1-\epsilon _\mathrm{r}) \Delta m \left[\frac{w_i m_i}{\sum _j (w_j m_j)}\right], \end{eqnarray}$$where wi is the kernel weight of particle i, mi its mass, and the sum is over all neighbours; as above, the factor of (1 − ϵr) accounts for the mass converted to energy. In addition to mass, a fraction δmi/mi of the momentum of neighbour i is also transferred to the SMBH. To prevent individual gas particles from becoming too light, we exclude any neighbour that would be reduced to less than half its initial mass and accept that the dynamical mass of the SMBH grows slightly less than desired in this case. In practice, we have found that this limit is never reached in the simulations presented here, because stars typically inject far more mass into gas particles than is drained by SMBHs.

2017ApJ...834...20A__Temi_et_al._2007a_Instance_1

Lenticular galaxies seems to have a wider range of properties compared to ellipticals that resemble more the old definition of ETGs. However, even in ellipticals, large differences prevail. Recent observations of elliptical galaxies with Spitzer and Herschel (Temi et al. 2005, 2007a, 2007b, 2009; Smith et al. 2012; Agius et al. 2013; Mathews et al. 2013) have revealed that the far-infrared (FIR) luminosity LFIR from these galaxies can vary by ∼100 among galaxies with similar optical luminosity. The 70 μm band luminosities (from Temi et al. 2007a, 2009), is a good example of such a huge scatter in the FIR luminosity of elliptical galaxies. Some of the high L70 galaxies are members of a small subset of ellipticals having radio detections of neutral and molecular gas. A few others may be S0 galaxies which, because of their rotationally supported disks, often contain large masses of cold gas and dust. Ellipticals containing large excess masses of dust and cold gas probably result from significant galaxy mergers in the past. However a fraction of elliptical galaxies appear to be completely normal but L70 in these galaxies still ranges over a factor of ∼30, far larger than can be explained by uncertainties in the estimate of the FIR spectral energy distribution (SED) due to local stellar mass loss. While a significant fraction of the cold gas mass in low- to intermediate-mass ETGs is thought to have an external, merger-related origin (e.g., Davis et al. 2011), in the most massive ETGs the cold gas phases are presumably generated internally (Davis et al. 2011; David et al. 2014; Werner et al. 2014). Mergers with gas- and dust-rich galaxies have often been suggested for the origin of dust in all elliptical galaxies (e.g., Forbes 1991). Although the merger explanation is almost certainly correct in some cases, mergers cannot explain most of the observed scatter in L70. A crucial element in our understanding of the evolution of galaxies toward ETGs is the mutual role played by the major merging of galaxies and the secular star formation quenching. Neither of these scenarios yet accounts for all the observational evidence, and one could assume both contributing to some extent.

2020ApJ...905..111Z__Jirička_et_al._2001_Instance_2

Surveys of radio bursts in decimetric wavelengths is presented in papers by Isliker & Benz (1994) and Jirička et al. (2001), within 1–3 GHz and 0.8–2.0 GHz frequency ranges, respectively. Some of these bursts are still not well understood. This is a case of the slowly positively drifting bursts (SPDBs). They appear in groups or as single bursts, with a duration of an individual burst from 1 to several seconds and their frequency drift is lower than about 100 MHz s−1 (Jirička et al. 2001). The SPDBs seem to be similar to the reverse type III bursts (Aschwanden 2002) but their frequency drift is much smaller. The majority of observed SPDBs are connected to solar flares (Jirička et al. 2001), and they appear many times at the very beginning of the flares (Benz & Simnett 1986; Kotrč et al. 1999; Kaltman et al. 2000; Karlický et al. 2018). Kaltman et al. (2000) reported on several SPDBs observed during three solar flares in the 0.8–2 GHz frequency range. They found frequency drifts of the observed SPDBs to be within the 20–180 MHz s−1 range. Kotrč et al. (1999) studied one of those flares. By combining the radio and spectral plus imaging Hα observations, they explained the observed SPDBs as radio emission generated by downwards propagating shock waves. Based on numerical simulations of the formation of thermal fronts in solar flares, Karlický (2015) proposed that SPDBs observed in the 1–2 GHz range could be a signature of a thermal front. Furthermore, Karlický et al. (2018) reported the observation of an SPDB (1.3–2.0 GHz) observed during the impulsive phase of an eruptive flare. They found time coincidence between the SPDB occurrence, an appearance of an ultraviolet (UV)/EUV multithermal plasma blob moving down along the dark Hα loop at approximately 280 km s−1, and the observed change of Hα profile at the footpoint of that dark loop. Combining these observations they concluded that observed SPDB was likely generated by the thermal front formed in front of the falling EUV blob.

2022MNRAS.513.4556Z__Swartz,_Wheeler_&_Harkness_1991_Instance_1

Type II supernovae (SNe II) are believed to originate from the core collapse of massive stars with initial masses larger than 8 M⊙ (Heger et al. 2003). They are characterized by prominent P-Cygni profiles of Balmer series in their spectra (Filippenko 1997). Photometrically, they are classified as SNe IIP if their light curves show extended plateau features (∼100 d), and SNe IIL if their light curves display post-peak linear declines (Barbon, Ciatti & Rosino 1979). Such a two-type classification is favoured by some statistical studies of SNe II (e.g. Faran et al. 2014a, b). However, Anderson et al. (2014) pointed out that if the sample is sufficiently large, one will find that SNe IIP and SNe IIL belong to a continuous distribution. Such a distribution is believed to be related to the mass of the hydrogen envelope maintained by the progenitor stars, as evidenced by the results that SNe having larger envelope masses produce light curves with shallower plateau slopes and longer plateau durations (Barbon et al. 1979; Swartz, Wheeler & Harkness 1991; Blinnikov & Bartunov 1993). In spite of the variety of envelope masses, the initial masses of the progenitor stars of SNe II are restricted to a certain range. Stellar evolution theory predicts an upper limit of 25 M⊙ for SN-II progenitors (Heger et al. 2003). Based on analyses of pre-explosion images (e.g. Smartt 2009), the progenitors of most SNe II are found to be red supergiants (RSGs), but their masses lie in a narrow range (i.e. ∼9–17 M⊙). This inconsistency is known as the ‘RSG problem’ and can be partially explained by the ‘failed SNe’ theory (Lovegrove & Woosley 2013). As SNe II can also be used to determine distances, thus they are intriguing to the cosmology community. The measurement methods include the Expanding Photosphere Method (EPM; Kirshner & Kwan 1974; Hamuy 2001) and the Standard Candle Method (SCM; Hamuy & Pinto 2002). The basic idea of EPM is to derive the intrinsic luminosity from the photospheric radius and the temperature, and then compare the intrinsic luminosity with the apparent value to obtain the distance, while the SCM is based on the correlation between the expansion velocity and the luminosity at a specific epoch.

2021ApJ...911...75M__Yamauchi_et_al._2004_Instance_1

With the advent of the Parker Solar Probe (PSP) we are able to directly measure properties of the solar wind closer to the Sun than ever before (Fox et al. 2016). One of the early highlight discoveries of PSP is the omnipresence of strong local deflections of the magnetic field in the solar wind, mostly referred to as switchbacks (Kasper et al. 2019; Horbury et al. 2020). These structures, also called folds, jets, or spikes, are not necessarily full reversals of the local magnetic field, unlike some names suggest. In fact, the measured distribution of deflections resembles a power law, with most of the deflections at small angles relative to the Parker spiral (Dudok de Wit et al. 2020). There is evidence that switchbacks are localized kinks in the magnetic field and not polarity reversals or closed loops (McManus et al. 2020; Whittlesey et al. 2020). However, switchbacks are not a new finding. They have already been observed for several decades, by, e.g., Helios (e.g., Horbury et al. 2018), out to 1.3 au by Ulysses (e.g., Balogh et al. 1999; Neugebauer & Goldstein 2013). The novelty in the PSP observations is their sharpness and omnipresence (among other features; see Dudok de Wit et al. 2020), indicating that switchbacks are a more frequent feature closer to the Sun. This observation lends the possibility that switchbacks may originate lower in the solar atmosphere. The formation mechanism(s) of switchbacks, and whether they represent large-amplitude Alfvén waves or structures advected by the solar wind, is as of yet not known. Several explanations about their origin have been put forth. Among these theories, interchange reconnection is the most focused upon (Yamauchi et al. 2004; Fisk 2005; Fisk & Kasper 2020). In this scenario, switchbacks are generated in the solar corona, and form at the reconnection sites between open and closed magnetic fluxes. In some studies, it is argued that the interchange reconnection results in magnetic flux ropes, which are ejected by the reconnection outflow, and are advected by the wind (Drake et al. 2020). In other studies, reconnection is thought to generate either Alfvénic (He et al. 2020) or fast magnetosonic (Zank et al. 2020) wave pulses. Alternatively, as the distribution of deflections appears to be featureless and monotone in switchbacks (Dudok de Wit et al. 2020), they may not be a distinct feature but a manifestation of the ensuing turbulent dynamics in the solar wind (Squire et al. 2020). Thus, it is still not clear whether switchbacks originate in the lower solar atmosphere, or represent dynamic features of solar wind turbulence. Additionally, it is not clear whether they are wavelike perturbations, propagating at the Alfvén or some other characteristic speed, or structures that are advected by the wind, such as flux ropes. However, some observations offer good constraints on the nature of switchbacks. Given that switchbacks are characterized by a strong Alfvénic correlation of their velocity and magnetic field perturbations, a nearly constant magnetic pressure, and velocity enhancements along the propagation direction, an interpretation in terms of propagating nonlinear Alfvén waves is a plausible scenario (Matteini et al. 2014). Alternatively, their localization in the perpendicular direction and the kink-like geometry may indicate their kink fast magnetoacoustic nature (e.g., Van Doorsselaere et al. 2008). An additional option is the association of the switchbacks with kink solitons, which keep their shape because of the balance between nonlinear and dispersive effects (see, e.g., Ruderman et al. 2010), i.e., a stationary nonlinear kink wave pulse. Other observations, such as a sharp rise in ion temperature at the boundaries of switchbacks are more compatible with an origin by reconnection (Farrell et al. 2020; Mozer et al. 2020).

2021ApJ...917...59C__Schiller_et_al._2020_Instance_1

With regard to Ni isotopes in the metal phase of iron meteorites, multiple studies have analyzed samples belonging to the same iron groups (e.g., IIAB, IIIAB, IVA, IVB) using broadly similar techniques (i.e., wet chemical separation of Ni followed by mass spectrometry). The initial studies reported no nucleosynthetic anomalies (Cook et al. 2006; Quitté et al. 2006). As analytical precision improved, anomalies were reported in some subsequent studies (e.g., Bizzarro et al. 2007; Regelous et al. 2008; Steele et al. 2011) but not in others (e.g., Dauphas et al. 2008; Chen et al. 2009), and no clear picture has emerged. Moreover, the interpretation of Ni isotopic data is hampered by the omission of data for 64Ni, the least abundant Ni isotope, from multiple studies. For Fe isotopes, initial studies pointed to a high degree of homogenization among various meteorite classes (e.g., Dauphas et al. 2008; Tang & Dauphas 2012). However, more recent studies (Cook & Schönbächler 2017; Schiller et al. 2020) have revealed small non-mass-dependent variations in Fe isotopes. A related issue, also without consensus, concerns the magnitude of the initial abundance of the radionuclide 60Fe in the early solar system. A wide variety of sample types have been analyzed in an attempt to constrain the solar system initial 60Fe/56Fe ratio including CAIs (e.g., Quitté et al. 2007), chondrules (e.g., Tachibana et al. 2006; Telus et al. 2018), chondritic metal (Cook et al. 2006), and differentiated meteorites, such as angrites and eucrites (e.g., Tang & Dauphas 2012, 2015). These studies have broadly used one of two analytical approaches: wet chemical separation of Ni followed by mass spectrometry or in situ analyses using ion microprobe (aka SIMS). Among these studies, estimates of 60Fe/56Fe span more than a factor of 100 (e.g., Quitté et al. 2007; Tang & Dauphas 2012, 2015) from ≈2 × 10−6 to ≈1 × 10−8. Determining the initial 60Fe/56Fe ratio in the protoplanetary disk is important for studies of early solar system chronology. Furthermore, radioactive decay of 60Fe may have been an important heat source on early-formed bodies, but this depends on its abundance (Kohman & Robison 1980; Yoshino et al. 2003).

2017ApJ...837...89W__Henry_et_al._2013_Instance_1

For over a decade, a tight correlation between metallicity and galaxy stellar mass ( ), i.e., the mass–metallicity relation (MZR), has been quantitatively established, from the vast database of local galaxies observed by the Sloan Digital Sky Survey (SDSS; Tremonti et al. 2004; Zahid et al. 2012; Andrews & Martini 2013). This relation has been further extended to high redshifts, using deep near-infrared (IR) spectroscopy facilitated by large ground-based and space-based telescopes (Erb et al. 2006; Maiolino et al. 2008; Zahid et al. 2011; Henry et al. 2013; Steidel et al. 2014; Sanders et al. 2015; Guo et al. 2016). The measurements of the MZR as a function of redshift can cast useful constraints on various galaxy evolution models, since the slope of the MZR is sensitive to the properties of outflows, such as the mass loading factor and the outflow speed (see, e.g., Davé et al. 2012; Lu et al. 2015a). This slope can also be explained by variations of star-formation efficiency and gas mass fraction in galaxies with different stellar masses (see, e.g., Baldry et al. 2008; Zahid et al. 2014). The normalization of the MZR can shed light upon the stellar chemical yield across cosmic time (Finlator & Davé 2008). Mannucci et al. (2010) first suggested that there exists a so-called fundamental metallicity relation (FMR) in the 3D parameter space spanned by , star-formation rate (SFR), and metallicity, such that the MZR is merely a 2D projection of this more fundamental 3D manifold (see also Hunt et al. 2016). This 3D scaling relation shows a tight scatter (∼0.05 dex) in metallicity and is speculated to not evolve with z. In this context, the apparent redshift evolution of the MZR normalization originates primarily from sampling the FMR in terms of galaxies with different SFR. This concept of the FMR is in accord with the gas regulator model proposed by Lilly et al. (2013), even though mergers can also play a subtle role in shaping the form of the FMR by increasing the scatter (Michel-Dansac et al. 2008). However, at high redshifts, the validity of the FMR is still under investigation (see, e.g., Wuyts et al. 2014; Sanders et al. 2015).

2016ApJ...817..152X__Brien_et_al._2006_Instance_1

Afterglows of gamma-ray bursts (GRBs) are generally believed to be produced by a relativistic jet interacting with the surrounding medium (e.g., Mészáros & Rees 1997; Sari et al. 1998; Kumar & Zhang 2015). Very early multi-wavelength afterglows are critical to reveal the properties of the radiating fireball and its environment as well as the central engine of GRBs. Using the prompt slewing and precise locating capacity of the X-ray telescope (XRT) on board the Swift mission, very early X-ray and optical afterglows were obtained with the XRT and ground-based optical telescopes. The early X-ray afterglows are usually dominated by erratic X-ray flares and the tail emission of prompt gamma-rays are due to the arrival time delay of photons in the high latitude of the radiating fireball (Liang et al. 2006; O’Brien et al. 2006; Zhang et al. 2006, 2007a). The flares and prompt tail emission are usually not seen in the early optical afterglow data (Li et al. 2012; Wang et al. 2013). About one-third of well-sampled optical afterglow light curves show a clear smooth bump (Li et al. 2012). It may be attributed to deceleration of the fireball by the ambient medium (see Rees & Mészáros 1992; Mészáros & Rees 1993 and Sari & Piran 1999 for the thin shell case; Kobayashi et al. 1999 and Kobayashi & Zhang 2007 for the thick shell case). This may give the most robust estimate for the initial Lorentz factor ( ) of the fireball since the deceleration time weakly depends on other model parameters than (e.g., Molinari et al. 2007; Melandri et al. 2010; Xin et al. 2015). With a sample of GRBs with a detected optical afterglow onset bump, Liang et al. (2010) derived their and found a tight relation (see also a relation in Lu et al. 2012). Furthermore, by incorporating the peak energy of the spectrum in the burst frame ( ) into the relation, a tighter relation is found (Liang et al. 2015). This relation places strong constraints on the composition of GRB fireballs.

2022ApJ...926..151Z__Saxena_et_al._2020_Instance_1

Unlike the CMB, the 21 cm signal is highly non-Gaussian, because patchy, bubble-like structures of ionized hydrogen (H ii) regions are produced surrounding the ionizing sources. Thus, there is potentially a wealth of information in the 21 cm signal that is not contained in the 21 cm power spectrum, a two-point statistics of 21 cm brightness temperature fluctuations that is traditionally well studied in the literature. It is therefore essential to develop new methods that maximally exploit the full information in the 3D 21 cm images obtained by the SKA. Toward this goal, conventionally, new summary statistics that can only be measured with imaging have been proposed. These include the three-point correlation function (Hoffmann et al. 2019; Jennings et al. 2020), bispectrum (Yoshiura et al. 2015; Shimabukuro et al. 2016, 2017; Majumdar et al. 2018, 2020; Hutter et al. 2020; Saxena et al. 2020; Kamran et al. 2021), one-point statistics (Harker et al. 2009; Shimabukuro et al. 2015; Gorce et al. 2021), topological quantities such as the Minkowski functionals (Gleser et al. 2006; Chen et al. 2019; Kapahtia et al. 2021) and Betti numbers (Giri & Mellema 2021), the cross correlation between the 21 cm line and other probes, such as the CO line (Gong et al. 2011; Lidz et al. 2011), the C ii line (Gong et al. 2012; Beane & Lidz 2018), the kinetic Sunyaev–Zel’dovich (kSZ) effect (Ma et al. 2018; La Plante et al. 2020), and novel techniques such as the antisymmetric cross correlation between the 21 cm line and CO line (Zhou et al. 2021). Since those summary statistics are fully determined by the parameters in the reionization models (hereafter “reionization parameters”), in principle, Monte Carlo Markov Chain (MCMC) methods can be employed to constrain the reionization parameters from measurements of those statistics with futuristic 21 cm experiments (see, e.g., Watkinson et al. 2022), just as the MCMC analysis with the 21 cm power spectrum (Greig & Mesinger 2015, 2017, 2018).

2020MNRAS.499.1788W__Wolfire_et_al._2003_Instance_1

Owing to their brightness at rest-frame FIR wavelengths, the ionized and neutral species of Carbon, Nitrogen, and Oxygen are powerful diagnostic lines for tracing the ISM of nearby and distant galaxies. When combined with photodissociation region (PDR) models (Tielens & Hollenbach 1985), measurement of the emission from different lines provide a means to constrain quantities such as the ionization rate and metallicity of the ISM of galaxies. Multilevel FIR transition lines have now been widely surveyed in local galaxies, originally by ISO (e.g. Luhman et al. 1998; Malhotra et al. 2001) and more recently using the PACS spectrometer (Poglitsch et al. 2010) on the ESA Herschel Space Observatory (Pilbratt et al. 2010). The [C ii]158 μm line is typically the brightest in star-forming galaxies, arising from ionized, and even neutral gas where it is the main coolant (Wolfire et al. 2003). Another commonly observed FIR line tracer of the ionized ISM is [N ii]122 μm. It has the advantage that it can be found associated with lower excitation gas, close to that observed in our own Galaxy (e.g. Goldsmith et al. 2015; Herrera-Camus et al. 2016). Another major coolant of the ISM is [O i]63 μm (Wolfire et al. 2003). Owing to its high excitation temperature and critical density, it can dominate the cooling in regions of starburst activity (Kaufman et al. 1999; Kaufman, Wolfire & Hollenbach 2006; Brauher, Dale & Helou 2008; Croxall et al. 2012; Narayanan & Krumholz 2017). When combined with measurements of the [C ii]158 μm line intensity and FIR luminosity, the [O i]63 μm line intensity can constrain the FUV field, G, and the gas density using PDR models. The luminosity in these FIR lines generally exhibits a deficit in the most FIR luminous galaxies compared to the trend expected from lower luminosity galaxies (e.g. Malhotra et al. 2001; Graciá-Carpio et al. 2011; Díaz-Santos et al. 2017). This has made the emission from lines like [O i]63 μm more challenging to detect at high-redshifts.

2021MNRAS.508.2823C__Baumgardt,_Makino_&_Ebisuzaki_2004_Instance_1

Globular clusters are of particular dynamical interest because of the phenomena of core collapse and binary burning. Stellar interactions in dense globular clusters tend to drive the central core towards a collapse to a state of extremely small radius and high density. Primordial binary populations may delay the onset of this core collapse by serving as dynamical energy sources, but eventually become depleted by various destruction mechanisms (e.g. Verbunt & Freire 2014), allowing core collapse to proceed (e.g. Fregeau et al. 2003, and references therein). Several studies suggest that black hole binaries, rather than main-sequence (MS) binaries, are critical for delaying core collapse (e.g. Breen & Heggie 2013; Wang et al. 2016; Kremer et al. 2019). In addition, an intermediate-mass black hole (IMBH) in a cluster core will act as a strong central energy source able to delay, or even prevent, core collapse (e.g. Baumgardt, Makino & Ebisuzaki 2004; Gill et al. 2008; Lützgendorf, Baumgardt & Kruijssen 2013). In any case, after an initial collapse, the cluster core will undergo gravothermal oscillations of expansion and contraction, as first demonstrated by Sugimoto & Bettwieser (1983), during which the core radius remains quite small. About 20–25 globular clusters, including NGC 6397 and NGC 6752, have very compact (rc ≲ 10 arcsec), high-density cores that appear to be post-collapse. The post-collapse oscillations of these cores should produce episodic bursts of strongly enhanced dynamical binary formation and ejection during the densest phases, as noted by Lugger et al. (2007). Their reasoning is based on the scaling of the encounter rate Γ, which is given by the integral of ρ2/v over the cluster volume, where ρ is spatial mass density and v is the velocity dispersion (Verbunt & Hut 1987; Bahramian et al. 2013). The encounter rate can be approximated by the simplified expression $\Gamma \propto \rho _0^2 r_c^3/v_0$ (Pooley et al. 2003), where ρ0 is central density, rc is the core radius, and v0 is the central velocity dispersion. This results in $\Gamma \propto r_c^{-1.4}$ for a simple homologous model for core collapse, in which $\rho _0 \propto r_c^{-2.2}$ and $v_0 \propto r_c^{-0.05}$ (Cohn 1980).

2022MNRAS.512.3137Z__Katz_et_al._1999_Instance_3

However, it is not straightforward to explain H2 formation in astronomical sources even when the catalytic roles of dust grains are introduced into models. Interstellar species are believed to be formed on cold grain surfaces via the so called Langmuir–Hinshelwood mechanism (Watson & Salpeter 1972; Pickles & Williams 1977; Hasegawa, Herbst & Leung 1992). To form H2, H atoms accrete on dust grains and then bind weakly with surfaces, which is known as physisorption. They can overcome the diffusion barrier and move on the grain surfaces via quantum tunnelling or thermal hopping. However, laboratory and theorectical studies showed that quantum tunnelling does not contribute much to the mobility of H atoms on silicate, carbonaceous or solid amorphous water (ASW) surfaces (Pirronello et al. 1997, 1999; Katz et al. 1999; Nyman 2021). If H atoms encounter other H atoms, then H2 molecules are formed. But H atoms can also desorb and leave grain surfaces. A hydrogen atom must reside on a grain long enough to find a partner H atom to form H2. As the dust temperature increases, the H atom desorption and diffusion rates also increase. So the temperature of grain surfaces must be sufficiently low so that an H atom can encounter another one before it desorbs. On the other hand, the temperature of grain surfaces must be high enough so that H atoms can diffuse on the grain surface. The parameter that measures how strongly species are to bound to grain surfaces is called desorption energy. It was found that if we assume a single H desorption energy in models, the dust temperature range over which efficient H2 formation occurs is narrow (6–10 K for olivine grains) (Katz et al. 1999). Moreover, it was found that the highest dust temperature at which H2 can be formed efficiently is less than 17 K (Katz et al. 1999). However, the grain surface temperature in the unshielded diffuse clouds, where hydrogen molecules are believed to be efficiently formed, is around 20 K (Li & Draine 2001).

2016ApJ...817...12P__Chamandy_et_al._2014_Instance_2

Large-scale magnetic fields with strength of the order of 1–10 μG have been observed in disk galaxies (e.g., Beck et al. 1996; Fletcher 2010; Beck 2012; Beck & Wielebinski 2013; Van Eck et al. 2015). The origin of these fields can be explained through mean-field dynamo theory (Ruzmaikin et al. 1988; Beck et al. 1996; Brandenburg & Subramanian 2005a; Kulsrud & Zweibel 2008). The conservation of magnetic helicity is one of the key constraints in these models, and also leads to the suppression of the α-effect. The operation of the mean-field dynamo automatically leads to the growth of magnetic helicity of opposite signs between the large-scale and small-scale magnetic fields (Pouquet et al. 1976; Gruzinov & Diamond 1994; Blackman & Field 2002). To avoid catastrophic suppression of the dynamo action (α-quenching), the magnetic helicity due to the small-scale magnetic field should be removed from the system (Blackman & Field 2000, 2001; Kleeorin et al. 2000). Mechanisms suggested to produce these small-scale magnetic helicity fluxes are: advection of magnetic fields by an outflow from the disk through the galactic fountain or wind (Shukurov et al. 2006; Sur et al. 2007; Chamandy et al. 2014), magnetic helicity flux from anisotropy of the turbulence produced by differential rotation (Vishniac & Cho 2001; Subramanian & Brandenburg 2004, 2006; Sur et al. 2007; Vishniac & Shapovalov 2014), and through diffusive flux (Kleeorin et al. 2000, 2002; Brandenburg et al. 2009; Mitra et al. 2010; Chamandy et al. 2014). The outflow of magnetic helicity from the disk through dynamo operation leads to the formation of a corona (Blackman & Field 2000). According to Taylor's hypothesis, an infinitely conducting corona would resistively relax to force-free field configurations under the constraint of global magnetic helicity conservation (Woltjer 1960; Taylor 1974; Finn & Antonsen 1983; Berger & Field 1984; Mangalam & Krishan 2000). In this paper, we include advective and diffusive fluxes in a simple semi-analytic model of a galactic dynamo that transfers magnetic helicity outside the disk and consequently builds up a force-free corona in course of time. We first solve the time-dependent dynamo equations by expressing them as separable in variables r and z. The radial part of the dynamo equation is solved using an eigenvector expansion constructed using the steady-state solutions of the dynamo equation. The eigenvalues of the z part of the solution are obtained by solving a fourth-order algebraic equation, which primarily depends upon the turbulence parameters and the magnetic helicity fluxes. Once the dynamo solutions are written out as parametric functions of these parameters, the evolution of the mean magnetic field is computed numerically by simultaneously solving the dynamical equations for α-quenching and the growth of large-scale coronal magnetic helicity. Since the large-scale magnetic field lines cross the boundary between the galactic disk and the corona, the magnetic helicity of the large-scale magnetic field in the disk volume is not well defined. Hence we use the concept of gauge-invariant relative helicity (Finn & Antonsen 1983; Berger & Field 1984; Berger 1985) to estimate the large-scale magnetic helicity in the disk and the corona. Here the gauge-invariant relative helicity for the cylindrical geometry is calculated using the prescription given in Low (2006, 2011). We then investigate the dependence of the saturated mean magnetic field strength and its geometry on the magnetic helicity fluxes within the disk and the corresponding evolution of the force-free field in the corona.

2017AandA...605A.102C__Konstantinova-Antova_et_al._2013_Instance_1

Magnetic fields are actively searched for at the surface of all kinds of stars throughout the Hertzsprung-Russell diagram (HRD), as they probably impact stellar evolution from birth to death in various ways (see e.g., Donati & Landstreet 2009 for a general review; and Wade et al. 2016; Alecian et al. 2013; Vidotto et al. 2014; Folsom et al. 2016 for recent spectropolarimetric surveys on massive, intermediate-mass, and low-mass stars). Recently, magnetic fields have been unambiguously detected via Zeeman signatures in a large sample of single G-K giants observed with the spectropolatimeters TBL/Narval and CFHT/ESPaDOnS (Konstantinova-Antova et al. 2013; Aurière et al. 2015; Borisova et al. 2016; Tsvetkova et al. 2017). Interestingly, the cool intermediate-mass evolved stars with surface magnetic fields are found to cluster in certain regions of the HRD that correspond to precise moments of their evolution where convective envelopes make up a significant fraction of the stellar mass. These observations are of particular importance for stellar-evolution modeling, since magnetic fields may play a crucial role in the angular momentum evolution of different types of stars. For instance, it is well established that magnetic fields affect the rotation rate of solar-type stars through the torque applied to stellar surfaces by magnetically coupled stellar winds (e.g., Schatzman 1962; Matt et al. 2015; Réville et al. 2015; Amard et al. 2016). However, the efficiency and the impact of magnetic braking for evolved stars are not well studied yet. In addition, because of the angular momentum they transport (e.g., Spruit 1999; Mathis & Zahn 2005), magnetic fields may play an important role in explaining the properties of core and surface rotation in giants as seen by asteroseismology (e.g., Beck et al. 2012; Mosser et al. 2012b,a; Deheuvels et al. 2012, 2014; Cantiello et al. 2014; Di Mauro et al. 2016), as well as the rotation rate of white dwarf remnants (Suijs et al. 2008). By comparing the observed rotational and magnetic properties of stars at different evolutionary phases with the predictions of rotating stellar models, one may thus obtain strong constraints on the input physics of the stellar models. Conversely, it is important to know whether the possible presence and the global properties of the magnetic field of a given star can be anticipated from its position in the Hertzsprung-Russell diagram (see Gregory et al. 2012 for the case of pre-main sequence stars).

2019MNRAS.488.5748W__Mao_&_Schneider_1998_Instance_1

The search for observational evidence of dark matter substructure in galaxies is on-going, as traditional methods for in-directly detecting dark matter sub-haloes (e.g. modelling tidal streams and gravitational lensing) have yet to agree on either the Milky Way’s or an external galaxies’ current substructure composition. Gravitational lensing allows for constraints to be placed on the dark matter substructure content of external galaxies, as orbiting sub-haloes will result in anomalies in strong gravitational lenses (Mao & Schneider 1998; Dalal & Kochanek 2002; Vegetti et al. 2012). The disruption of stellar streams by dark matter sub-haloes in the Milky Way has also been well studied (e.g. Johnston, Spergel & Haydn 2002; Ibata et al. 2002; Carlberg 2009; Erkal et al. 2016; Sanders, Bovy & Erkal 2016; Bovy, Erkal & Sanders 2017; Carlberg 2017), with gaps in stellar streams believed to be a tell-tale sign that a stream has recently encountered a sub-halo. Hence the presence of gaps in a tidal stream, or lack thereof, can be used to constrain the dark-matter substructure properties of the Milky Way (Yoon, Johnston & Hogg 2011; Carlberg 2012; Erkal & Belokurov 2015a,b; Bovy 2016; Carlberg 2016; Banik et al. 2018; Bonaca et al. 2019). However gaps in tidal streams, as well as overdensities and asymmetry, can also be produced as stars are tidally stripped from a star cluster along its orbit (Küpper et al. 2010), disc shocking (Odenkirchen et al. 2003), spiral arms (Dehnen et al. 2004), a tri-axial halo (Küpper et al. 2015), the Galactic bar (Pearson, Price-Whelan & Johnston 2017), interactions with giant molecular clouds (Amorisco et al. 2016), the stream’s progenitor cluster (Webb & Bovy 2018), and complex dynamical histories (i.e a time-dependent tidal field due to galaxy growth via mergers or accretion) (Carlberg 2018). Banik & Bovy (2018) recently studied these effects in detail for the Pal 5 stream and found that the bar and molecular clouds can each individually explain the observed structure of the Pal 5 stream. Hence new methods are required in order to help search for dark matter substructure in the Milky Way and constrain its properties.

2021MNRAS.506.1978L__Krivov_et_al._2018_Instance_1

This dynamical limit on planetesimal sizes could suggest that these may exist in the belt a factor of 103 larger than the ${\sim }1\,$km sized lower limit predicted by collisional replenishment. However, if the assumed α = 3.5 Dohnanyi (1969) size distribution continued up to planetesimals of this size, then the disc mass would be $M_{\rm {disc}}{\sim }220\, M_{\oplus }$, a factor of ∼3 higher than the prediction of Schüppler et al. (2016) and over an order of magnitude larger than the lower limit derived from the collisional lifetime and age of the system. This would not violate our earlier calculation since these ≫1 km planetesimals could be abundant in the disc without having collided within the age of the system. Whilst this higher total disc mass is still consistent with the dust mass measurements of protoplanetary discs (for example, see Andrews & Williams 2005; Ansdell et al. 2016; Cieza et al. 2019), high debris disc masses, i.e. those in the range of $100\!-\!1000\, M_{\oplus }$, become problematic since these would require a very high efficiency of primordial dust being incorporated into these larger planetesimals (an example of the so-called ‘disc mass problem’, see Krivov et al. 2018; Krivov & Wyatt 2021). Nevertheless, it might still be possible to explain the observed level of stirring by embedded bodies while retaining a lower disc mass (i.e. if the largest bodies are less frequent than the α = 3.5 size distribution would predict). If instead the size distribution had a slope of α = 3.7, then even with these 1200 km bodies, the total disc mass estimate would be reduced from ${\sim }220\, M_\oplus$ to ${\sim }6\, M_\oplus$, since the total number of these would be greatly reduced. On the other hand, the size distribution could be truncated, being much steeper for planetesimals larger than a few kilometres, and shallower in other regions. We estimate the effect that this can have on the derived disc mass using equation (9) of Krivov & Wyatt (2021), and find that the total mass can be reduced by an order of magnitude from ${\sim }220\, M_\oplus$ to ${\sim }17\, M_\oplus$, if based on a triple power law size distribution, with qmed = 4, qbig = 3 and q = 3.5. Alternatively the measured vertical scale height could be due to other dynamical interactions (e.g. stirring by a planet internal or external to the belt, or a recent stellar fly-by) or even be a remnant of the primordial disc (e.g. if this disc was born stirred, Booth & Clarke 2016). In summary, whilst q1 Eri at an age of ${\sim }1.4\, \rm {Gyr}$ is an outlier in terms of its brightness, it need not be an outlier in terms of its disc mass unless many much larger planetesimals are present.

2015AandA...576A...5C__Jørgensen_et_al._2012_Instance_1

The relative abundances of the three species are derived from the column densities in Table 2 and are compared with other star-forming regions and comets in Table 3. The (CH2OH)2/CH2OHCHO abundance ratio of ~0.3–0.5 previously derived in IRAS 16293 by Jørgensen et al. (2012) was revised. Indeed, the assignment in Jørgensen et al. (2012) was based on only one line of the gGg′ conformer of ethylene glycol about 200 cm-1 (~290 K, Müller & Christen 2004) above the lowest-energy aGg′ conformer – and thus tentative. An analysis from observations of six transitions of the lower energy conformer from ALMA Cycle 1 observations at 3 mm (four spectral windows at 89.48–89.73, 92.77–93.03, 102.48–102.73, and 103.18–103.42 GHz; Jørgensen et al., in prep.) results in a higher ethylene glycol-to-glycolaldehyde abundance ratio of 1.0 ± 0.3. This new estimate is consistent with the ratio expected between the aGg′ and gGg′ conformers under thermal equilibrium conditions at 300 K, the excitation temperature of glycolaldehyde derived in IRAS 16293 (Jørgensen et al. 2012). The (CH2OH)2/CH2OHCHO abundance ratio in IRAS2A is estimated at 5.5 ± 1.0 if we consider the column densities derived from the rotational diagrams. It is slightly lower (4.6), however, if we use the column density of ethylene glycol of 1.1 × 1016 cm-2 that does not overproduce the peak intensities of a few lines (see Fig. 3). The (CH2OH)2/CH2OHCHO abundance ratio consequently is a factor ~5 higher than in the Class 0 protostar IRAS 16293. It is also higher than in the other star-forming regions (see Table 3), but similar to the lower limits derived in comets (≳3–6). This indicates that the glycolaldehyde chemistry may in general vary among hot corinos. It is possible that like IRAS2A, other very young low-mass protostars show high (CH2OH)2/CH2OHCHO abundance ratios, in agreement with the cometary values. The CH3OCHO/CH2OHCHO column density ratio found in IRAS2A (~20) ranges between the values derived in the molecular clouds from the Galactic center (~3.3–5.2) and the high-mass star-forming regions (~40–52). A lower limit of 2 was derived for comet Hale-Bopp.

2015ApJ...799...99K__Coelho_&_Gadotti_2011_Instance_2

The nonaxisymmetric potential of a bar induces large-scale streaming motions in stars and gas into the central part of the galaxy (e.g., Athanassoula 1992a, 1992b; Sellwood & Wilkinson 1993). Being dissipative, the gas loses angular momentum and energy and flows inward toward the galactic center (Knapen et al. 1995; Regan et al. 1999; Sheth et al. 2000, 2002), accumulating in the central ∼1 kpc of galaxies (e.g., Sakamoto et al. 1999; Sheth et al. 2005). The accumulation of gas in the central parts leads to high levels of circumnuclear star formation activity (Sérsic & Pastoriza 1965; Hawarden et al. 1986; Devereux 1987; Martin 1995; Ho et al. 1997; Sheth et al. 2000, 2005; Gadotti & dos Anjos 2001; Ellison et al. 2011; Coelho & Gadotti 2011; Wang et al. 2012); this circumnuclear star formation is often in the shape of nuclear rings (Knapen et al. 2002; Comerón et al. 2010; Kim et al. 2012; Seo & Kim 2013) and nuclear star clusters (Böker et al. 2002, 2004, 2011). Such star formation activities may help to create disky pseudobulges (Kormendy & Kennicutt 2004; Sheth et al. 2005; Athanassoula 2005; Debattista et al. 2006). Bars are the primary mechanism for transporting gas from a few kiloparsec scale to the central kiloparsec. However, there have been mixed answers to the question of whether the presence of a bar and active galactic nucleus (AGN) activity are connected. Some studies find weak statistical links among AGN activity and the presence of bars (e.g., Arsenault 1989; Knapen et al. 2000; Laine et al. 2002; Laurikainen et al. 2004a; Coelho & Gadotti 2011), whereas others find little or no link (e.g., Moles et al. 1995; McLeod & Rieke 1995; Mulchaey & Regan 1997; Ho et al. 1997; Hunt & Malkan 1999; Martini et al. 2003; Lee et al. 2012; Cisternas et al. 2013). While bar torques drive gas inside the bar corotation inward, they push the gas between the corotation and outer Lindblad resonance (OLR) outward (Combes 2008; Kubryk et al. 2013).

2018MNRAS.479.4073L__Chatterjee_et_al._2008_Instance_1

As can be seen from Fig. 1, in both the optical and the NIR band, several predominant flares appearing in the whole period considered are usually surrounded by other smaller subflares. In addition, it seems that the NIR light curves follow a similar flaring activity, lasting for several days or weeks, to the optical bands, although the NIR flux is more variable compared than the optical one, increasing by about 7 times in the J band, from 9.68 to 67.85 mJy (for an example, see Table 1). An exponential rise/decay model can appropriately fit the blazar light curve, which generally consists of superpositions of a steady background flux density and a number of flare components caused by events in the jet or the accretion disc/corona region (e.g. Valtaoja et al. 1999; Chatterjee et al. 2008; Abdo et al. 2010b; Chatterjee et al. 2012; Guo et al. 2016; Li et al. 2017, 2018, and references therein). Following Abdo et al. (2010b), we define the multipeaked model to analyse the temporal flare profiles during the interval of interest: (3) \begin{eqnarray*} F(t) = {F_{\rm c}} + \sum _{i}\left[{F_i}{\left({{{\rm e}^{\frac{{{T_{0,i}} - t}}{{{T_{r,i}}}}}} + {{\rm e}^{\frac{{{\rm t} - {T_{0,i}}}}{{{T_{{\rm d},i}}}}}}} \right)^{ - 1}}\right], \end{eqnarray*} where Fc is the assumed constant background flux level underlying the flare components and is constrained to be less than or equal to the lowest value of flux, and the sum runs over all of the individual flares being fitted. For any one of the individual flares, Fi is the variability amplitude, T0, i is the epoch of the peak, and Tr, i and Td, i are the time-scales of rise and decay, respectively. Equation (3) represents flares within a limited time interval, because the observational gaps exist regularly or occasionally. In order to ensure the accuracy of the fitting model, the divided observational segments in different bands mentioned in Section 2 were used to analyse the temporal flare profiles. Each segment of the light curves was first interpolated by means of cubic spline interpolations through the 1-d binned light curves, then smoothed by a five-point moving-average filter to reduce the impact of the short-term variabilities.

2022MNRAS.511.4946N__Vivek,_Srianand_&_Gupta_2016_Instance_1

Outflows from the central regions of active galactic nuclei (AGNs) are thought to be the main agents that regulate the evolution of both the central supermassive black holes as well as the host galaxies (Silk & Rees 1998; Di Matteo, Springel & Hernquist 2005). The presence of high-velocity outflows from AGNs can be established from the evidence provided by the blueshifted broad absorption lines (BALs) seen in the spectra of 10–20 per cent of quasi-stellar objects (QSOs)1 (Weymann et al. 1991). BALQSOs are classified into three sub-classes based on the ionization state of the absorbing gas: (a) high-ionization BALQSOs (HiBALs) consist of absorption from high-ionization lines such as C iv, Si iv, and N v (Gibson et al. 2008; Filiz Ak et al. 2013; Vivek, Srianand & Gupta 2016; Vivek 2019; Mishra et al. 2021) (b) low-ionization BALQSOs (LoBALs) show absorption from low-ionization lines such as Mg ii and Al iii along with the high-ionization lines (Voit, Weymann & Korista 1993; Vivek et al. 2014; Vivek, Srianand & Dawson 2018; Yi et al. 2019), and (c) iron-LoBALs (FeLoBALs) are LoBALs with excited fine-structure Fe ii and/or Fe iii absorption lines (Vivek et al. 2012; McGraw et al. 2015). The observed BALQSO fraction is explained either by an orientation model, where the line of sight intersects with the BAL absorbing clouds in 10–20 per cent QSOs (Weymann et al. 1991; Elvis 2000), or an evolutionary model, where the QSO spends 10–20 per cent of its lifetime in the BALQSO phase (Farrah et al. 2007; Lipari et al. 2009). Although the similarity in the optical/NIR properties of BAL and non-BALQSOs, higher reddening and polarization features in BALQSOs support the orientation model, the observed radio spectral indices of BALQSOs and the discovery of polar winds in BALQSOs challenge this scenario (Reichard et al. 2003; Zhou et al. 2006; Wang, Wang & Wang 2007; Montenegro-Montes et al. 2008). On the other hand, the anticorrelation between the radio loudness parameter and balnicity index, Compact Steep Spectrum (CSS) nature of BALQSOs, and the redshift dependence of BALQSO fraction favour the evolutionary model (Gregg, Becker & de Vries 2006; Farrah et al. 2007; Allen et al. 2011).

2021MNRAS.503.1319G__Bolton_et_al._2008_Instance_1

In this paper, following the method proposed by Ofek et al. (2003), we use the differential optical depth to lensing with respect to the lens redshift zl as the probability density. For a statistical sample that contains Nl strong lensing systems, the log-likelihood of observing the lens at redshift zl is given by (9)$$\begin{eqnarray*} {\rm ln}\mathcal {L}({\bf p})=\sum _{i=1}^{N_l} {\rm ln} \delta p_i({\bf p}), \end{eqnarray*}$$where p represents the set of the VDF parameters (α, β) and the galaxy evolution parameters (νn, νv). Now one can perform Monte Carlo simulations of the posterior likelihood ${\cal L} \sim \exp {(- \chi ^2 / 2)}$, where the χ2 function is defined as (10)$$\begin{eqnarray*} \chi ^2=-2{\rm ln}\mathcal {L}. \end{eqnarray*}$$in our statistical analysis of lens redshift distribution. The sample used in this paper is primarily drawn from Sloan Lens ACS Survey (SLACS) and recent large-scale observations of galaxies, which is compiled and summarized in Cao et al. (2015) and Shu et al. (2017). The combined sample includes 91 lenses from SLACS (Bolton et al. 2008; Auger et al. 2009; Shu et al. 2017) and an extension of the SLACS survey known as ‘SLACS for the Masses’ (S4TM) (Shu et al. 2015, 2017), 35 lenses from the BOSS emission-line lens survey (BELLS) (Brownstein et al. 2012) and BELLS for GALaxy-Lyα EmitteR sYstemsGALLERY (BELLS GALLERY) (Shu et al. 2016a,b), 26 lenses from the Strong Lensing Legacy Survey (SL2S) (Sonnenfeld et al. 2013a,b), and five lenses from Lenses Structure and Dynamic (LSD) (Treu & Koopmans 2002; Koopmans & Treu 2003; Treu & Koopmans 2004). The advantage of this recently assembled lens sample, the detailed information of which is described and listed in Chen et al. (2019), lies in its well-defined observational selection criteria satisfying the assumption of spherical lens mass model. Fig. 1 shows the redshift distributions of the lensing systems used in our analysis. However, a statistical analysis requires a sample that is complete and has well-characterized, homogeneous selection criteria. Note that the lensing systems collected in this analysis are selected in very different manners. For instance, the SLACS, S4TM, and BELLS surveys, respectively, selected candidates from the spectroscopic observations of ETGs and look for the presence of higher redshift emission lines in Sloan Digital Sky Survey I (Eisenstein et al. 2001) and Sloan Digital Sky Survey-III (Eisenstein et al. 2011). These lens candidates were followed up with HST ACS snapshot imaging and after image processing. Therefore, in order to verify the completeness of the full early-type lens sample (hereafter Sample A), one additional subsample will also be applied to discuss its utility for the redshift test: 126 deflector-selected lenses from SLACS, S4TM, BELLS, and BELLS GALLERY (hereafter Sample B). Such choice is also motivated by the fact that the SLACS and BELLS lenses could be moderately suffered from the finite Sloan fibre size (Brownstein et al. 2012).

2018AandA...611A..74R__Grady_et_al._2013_Instance_3

In this context, MWC 758 (HD 36112) offers a unique environment to probe the existence of planetary companions and to explore the connection between disk structures and planet formation. MWC 758 is a young stellar object (3.5 ± 2 Myr, Meeus et al. 2012) at a distance of 151 $^{+9}_{-8}$ 151 −9 +8 4 pc (Gaia Collaboration 2016) close to the edge of the Taurus star forming region (stellar properties are given in Table 1). Measurements of resolved CO emission around the star determined the stellar mass to be 2.0 ± 0.2 M⊙ and the disk to have an inclination of 21° ± 2° and a position angle of the semi-major axis of 65° ± 7° (Isella et al. 2010). The mass and age estimates were based on the previously adopted hipparcos distances of 200 pc (van den Ancker et al. 1998) and 279 pc (van Leeuwen 2007). Given the revised Gaia distance, the star could be older and lighter than previously thought. In this paper, we assume a stellar mass of 1.5 ±0.2 M⊙, reflecting the scaling of the dynamical mass estimate to the new Gaia distance. Based on its SED, MWC 758 has been classified as a pre-transition disk (Grady et al. 2013). Although a cavity of 55 astronomical units (au) in radius has been inferred from dust millimeter emission (Andrews et al. 2011), infrared polarized intensity observations have found no clear evidence for a cavity in scattered light (Grady et al. 2013; Benisty et al. 2015). Using Ks-band (2.15 μm) direct imaging andH-band (1.65 μm) polarimetric imaging with the High-Contrast Instrument with Adaptive Optics (HiCIAO) at the Subaru Telescope, Grady et al. (2013) detected two spiral arms and polarized light down to 0.′′ 1 (15 au) from the star. Recent VLT Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE) observations in the Y band (1.04 μm) have confirmed the presence of scattered light at least down to 14 au (Benisty et al. 2015). The asymmetries observed by Isella et al. (2010) in the mm-dust distribution and in CO emission suggest that the disk may be gravitationally perturbed by a low-mass companion orbiting within a radius of 23 au (assuming a distance of 151 pc). The asymmetric cm-dust distribution was shown to follow the location of the mm-dust (Marino et al. 2015a), hinting towards the hypothesis of a dust trap, which could also be created by a companion in the gap through the Rossby wave instability (e.g., Pinilla et al. 2012b). Hydrodynamical simulations of the disk indicate that the observed spirals could instead be launched by a massive planet or brown dwarf at larger separations (~ 100 au based on the revised Gaia distance, Dong et al. 2015b). The presence of stellar companions down to a mass limit of 12 MJup at 0.′′ 25 and of planets outside 0.′′5 (5 MJup at 0.′′ 5, and 3 MJup at 1′′ , according to the BT-SETTL models; Allard et al. 2012) has been ruled out based on a combination of sparse aperture masking observations at L′ band and angular differential imaging at K′ and Ks bands (Grady et al. 2013).

2021AandA...650A.205V__Fontaine_et_al._2012_Instance_1

The formation of the approximately 40% of sdB stars that appear to be single has been a mystery for decades. In the absence of a companion, it is hard to explain how the star can expel most of its envelope on the RGB and still achieve core-He burning ignition. Recently, Pelisoli et al. (2020) suggested that all sdB stars might originate from binary evolution. Merger scenarios involving two low-mass white dwarfs have also been investigated (Webbink 1984; Han et al. 2002, 2003; Zhang & Jeffery 2012), but several facts challenge this hypothesis. First, compact low-mass white dwarf binaries are quite rare, even though some candidates are identified (Ratzloff et al. 2019). Second, the mass distributions of single and binary sdB stars are indistinguishable (Fontaine et al. 2012, Table 3 in particular). This mass distribution is mainly obtained from asteroseismology (some sdB stars exhibit oscillations, which allow the precise and accurate determination of the stellar parameters, including total mass; Van Grootel et al. 2013) and also from binary light-curve modeling for hot subdwarfs in eclipsing binary systems. Single and binary mass distributions peak at ~ 0.47 M⊙, which is the minimum core mass required to ignite He through a He-flash at the tip of RGB (stars of ≳ 2.3 M⊙ are able to ignite He quietly at lower core masses, down to ~0.33 M⊙, but the more massive the stars, the rarer they are). A mass distribution of single sdB stars from mergers, in contrast, would be much broader (0.4−0.7 M⊙; Han et al. 2002). With the DR2 release of Gaia (Gaia Collaboration 2018) and precise distances for many hot subdwarfs (Geier 2020), it is now also possible to build a spectrophotometric mass distribution for a much larger sample than what was achieved with the hot subdwarf pulsators or those in eclipsing binaries. Individual masses are much less precise than those obtained by asteroseismology or binary light-curve modeling (Schneider et al. 2019). However, single and binary spectrophotometric mass distributions share the same properties here as well, which tends to disprove the hypothesis of different origins for single and binary sdB stars. The third piece of evidence against merger scenarios (which would most likely result in fast-rotating objects) is the very slow rotation of almost all single sdB stars, as obtained through v sin i measurements (Geier & Heber 2012) or from asteroseismology (Charpinet et al. 2018). Moreover, their rotation rates are in direct line with the core rotation rates observed in RC stars (Mosser et al. 2012), which is another strong indication that these stars and the single sdB stars do share a same origin, that is, that they are post-RGB stars.

2016AandA...595A.106C__Korista_&_Goad_2000_Instance_1

The present HST-COS data were taken 20 days after the last XMM-Newton pointing (Kaastra et al. 2011) as the closing measurements of the campaign, which lasted in total about 100 days. Spectral coverage simultaneous to HST-COS was provided instead by both Chandra-LETGS (Ebrero et al. 2011) and Swift-XRT (Mehdipour et al. 2011). We used the average SED recorded by the XMM-Newton instruments 20–60 days before the HST-COS observation. The choice of SED is very important in the BLR modeling, as different lines respond to the continuum variations on different time scales (Korista & Goad 2000; Peterson et al. 2004). Reverberation mapping studies of Mrk 509 report that the delay of the Hβ with respect to the continuum is very long (about 80 days for Hβ, Carone et al. 1996; Peterson et al. 2004). However, higher ionization lines respond more quickly to the continuum variations. Taking as a reference the average Hβ/C iv delay ratio for NGC 5548 (Peterson et al. 2004), for which – contrary to Mrk 509 – a large set of line measurements is available, we obtain that the C iv line in Mrk 509 should respond in approximately 40 days. A similar (but shorter) time delay should apply to the Lyα line (Korista & Goad 2000). This delay falls in the time interval covered by the XMM-Newton data. Therefore, our choice of SED should be appropriate for the modeling of at least the main UV lines. Variability of the X-ray broad lines has been reported on time scales of years (Costantini et al. 2010); however, no short-term studies are available. We expect that the X-ray broad lines should respond promptly to the continuum variations, as they may be located up to three times closer to the black hole with respect to the UV lines (C07). During the XMM-Newton campaign the flux changed by 30% at most, with a minimal change in spectral shape (Sect. 3.1). The used SED should therefore represent what the BLR gas sees for the X-ray band. However, for the optical lines the used SED might be too luminous as we observed an increase in luminosity of about 30% during the XMM-Newton campaign and, as seen above, the time-delay of the optical lines may be large.

2022AandA...666L...5G__Esquej_et_al._2014_Instance_1

More recently, García-Bernete et al. (2022) found that the PAH molecules responsible for the 11.3 μm PAH emission band are more resilient in the hard environments often present in AGN. In particular, the authors found larger 11.3/7.7 μm and 11.3/6.2 μm PAH ratios in AGN-dominated systems compared to SF galaxies, indicating a larger fraction of neutral PAH molecules (as noted by Smith et al. 2007 using a sample of relatively weak AGN). However, these studies were limited by the spatial resolution (∼4″) and the low spectral resolution (R ∼ 60–130) of Spitzer/InfraRed Spectrograph (IRS). Previous sub-arcsecond angular resolution N-band (∼8–13 μm) ground-based spectroscopic studies investigated the 11.3 μm PAH feature in the nuclear and circumnuclear regions of AGN (e.g., Hönig et al. 2010; González-Martín et al. 2013; Alonso-Herrero et al. 2014, 2016; Ramos et al. 2014; Esquej et al. 2014; García-Bernete et al. 2015; Jensen et al. 2017; Esparza-Arredondo et al. 2018). However, these works were unable to provide definitive details regarding the effect of the AGN on the PAH molecules due to limited wavelength coverage and sensitivity. The changes in the PAH properties due to the presence of the AGN might be more prominent in their innermost regions of galaxies. Therefore, the unprecedented combination of high angular and spectral resolution (R ∼ 1500 − 3500) in the entire mid-IR range (4.9–28.1 μm) afforded by the James Webb Space Telescope (JWST)/Mid-Infrared Instrument (MIRI; Rieke et al. 2015; Wells et al. 2015; Wright et al. 2015) is key to investigating PAH properties. In this Letter we report on the first investigation of PAH emission in the nuclear regions of three luminous Seyfert (Sy) galaxies and compare them with emission from SF regions using JWST/MIRI Medium Resolution Spectrograph (MRS) data. This enables us, for the first time, to characterise the PAH properties of local luminous Sy galaxies (log (Lbol)> 44.46 erg s−1)1 at sub-arcsecond scales (∼0.45″, ∼142–245 pc).

2022AandA...661A..10B__Ghirardini_et_al._2021a_Instance_2

It is also possible that these clusters have a smaller extent and can just be missed by our extent selection as our detection algorithm sets the extent to zero if it is smaller than 6 (Brunner et al. 2022). Following the method presented in Ghirardini et al. (2021a), we estimated several dynamical properties of the clusters in the point source sample and compared them with the extent-selected sample presented in Ghirardini et al. (2021a). In Fig. 5 we compare the distributions of the core radii (Rcore) constrained by the V06 model and the concentration parameter (cSB) between these two samples. The concentration parameter is defined as the ratio of the surface brightness within 0.1 R500 to the surface brightness within R500 (Ghirardini et al. 2021a; Santos et al. 2008; Maughan et al. 2012). Intuitively, the expectation is that the smaller the core radius, the more compact the cluster. The left panel of Fig. 5 clearly shows that the clusters in the point source sample have relatively smaller core radii, hence the emission is more concentrated in a smaller area. Consistently, the concentration of the point source sample shows a clear excess in higher values than the extent-selected sample, indicating that a significantly larger fraction of cool-core clusters and clusters host a central AGN. We performed the same experiment by applying cuts in flux 1.5 × 10−14 ergs s−1 cm−2 and in detection likelihood to test whether the clusters are missed by the extent selection because they are fainter and/or more compact than the extent-selected clusters. The distribution of number density, core radius, and concentration parameter remains the same, indicating that the population of clusters in the point source catalog is more compact than the extent-selected sample. The extent-selected sample does not show a clear bias toward cool-core clusters or clusters with a central AGN, but contains the fraction of cool-cores is similar to that of SZ surveys (Ghirardini et al. 2021a). In this sample, we observe the opposite trend.

2016MNRAS.463.2348S__Narlikar_&_Padmanabhan_1991_Instance_1

We appear to live in a flat, homogeneous and isotropic expanding Universe (at scales >100 Mpc) well described by the Friedmann–Lemaître–Robertson–Walker (FLRW) metric where the scalefactor, a(t), describes the time dependence of the Universe geometry: a(t) ∝ t1/2 for radiation domination, a(t) ∝ t2/3 for matter domination, and $a(t)\propto \exp (\scriptstyle\sqrt{\frac{\Lambda }{3}}ct)$ for dark energy domination, where Λ = 3H0ΩΛc−2 is the cosmological constant (e.g. Beringer et al. 2012) – see Table 1. A common normalization of the FLRW metric defines the scalefactor equal to unity at the present time (a(t0) = 1; e.g. Liddle & Lyth 1993), yielding, for a Universe dominated by a positive cosmological constant (cf. Table 1) (5) \begin{eqnarray} a_{\Lambda }(t)=\exp \left(c\sqrt{\frac{\Lambda }{3}}(t-t_{0})\right),& t_{\Lambda }\le t\le t_{0} \:. \end{eqnarray} For the matter-dominated Universe, we have (6) \begin{eqnarray} a_{\text{m}}(t)=a_{\Lambda }(t_{\Lambda })\left(\frac{t}{t_{\Lambda }}\right)^{2/3}, \:\: t_{\text{eq}}\le t\le t_{\Lambda } \:. \end{eqnarray} Between the end of inflation at t = te ∼ 10−33 s; (e.g. Narlikar & Padmanabhan 1991) and t = teq = 2.37 × 1012 s, the Universe was radiation-dominated since at the latter instant a radiation-matter equality is reached (Table 1). Exceptions to radiation-domination in the te ≤ t ≤ teq time interval might have taken place during cosmological phase transitions such as the QCD (when the Universe might have been dust-like). We, then, split the radiation-domination epoch into three parts. For the interval between the end of the QCD phase transition (t+) and teq, we define (7) \begin{equation} a_{\text{rl}}(t)=a_{\text{m}}(t_{\text{eq}})\left(\frac{t}{t_{\text{eq}}}\right)^{1/2}, \:\: t_{+}\le t\le t_{\text{eq}} \:. \end{equation} During the QCD phase transition, we define (8) \begin{eqnarray} a_{\text{QCD}}(t)=a_{\text{rl}}(t_{+})\left(\frac{t}{t_{+}}\right)^{n_{\text{QCD}}}, \:\: t_{-}\le t\le t_{+}, \end{eqnarray} where nQCD = 2/3 if the Universe experiences a QCD dust-like phase or nQCD = 1/2 if the Universe continues to be radiation-dominated during that epoch. Finally, between the end of inflation (te) and the beginning of the QCD phase transition (t−), we define (9) \begin{equation} a_{\text{rm}}(t)=a_{\text{QCD}}(t_{-})\left(\frac{t}{t_{-}}\right)^{1/2}, \:\: t_{\text{e}}\le t\le t_{-} \:. \end{equation}

2021MNRAS.505..435S__Goodman_2009_Instance_1

Detections of ionic, atomic, and molecular species in exoplanetary atmospheres serve as a unique and strong diagnostic of those chemical and dynamical processes driving their formation and evolution. Their detection and abundance measurements could act as indicators of planetary formation scenarios and reveal connections to the primordial protoplanetary disc and the host star (Williams & Cieza 2011; Mordasini et al. 2016; Madhusudhan et al. 2017). Furthermore, discoveries of atmospheric chemical species allow us to better understand various thermodynamical processes and chemistry, winds in the upper atmosphere (Goodman 2009; Snellen et al. 2010; Brogi et al. 2016; Madhusudhan et al. 2016; Wyttenbach et al. 2020), and to probe planetary interiors and various bulk properties through their abundances (Kite et al. 2016; Thorngren & Fortney 2019; Madhusudhan et al. 2020). A whole host of ions, atoms, and molecules have been detected through a variety of, often complementary, techniques, such as differential spectrophotometry using low-to-mid resolution spectroscopy (e.g. Gibson et al. 2012, 2017; Deming et al. 2013; Kreidberg et al. 2014; Kirk et al. 2016; Nortmann et al. 2016), and high resolution spectroscopic techniques (e.g. Redfield et al. 2008; Snellen et al. 2008; Rodler, Lopez-Morales & Ribas 2012; Birkby et al. 2013; Hoeijmakers et al. 2015, 2018, 2020; Brogi et al. 2016; Birkby et al. 2017; Žák et al. 2019; Ehrenreich et al. 2020). To date, ionic species such as Fe ii and Ti ii (Hoeijmakers et al. 2019), atomic absorption from Na, K, H α, and He (e.g. Redfield et al. 2008; Sedaghati et al. 2016; Casasayas-Barris et al. 2017; Spake et al. 2018; Chen et al. 2020; Seidel et al. 2020), and molecules such as H2O, CH4, and CO (e.g. Konopacky et al. 2013; Brogi et al. 2014; Fraine et al. 2014; Barman et al. 2015; Sing et al. 2016) have been detected through the aforementioned techniques. Needless to say that this list of detected constituents is by no means exhaustive, nor that of methods employed to detect exoplanetary atmospheres. For instance, high-resolution imaging instruments such as SPHERE (Beuzit et al. 2019) and GRAVITY (Gravity Collaboration et al. 2017), both at the VLT (ESO’s Very Large Telescope), through combination with low-dispersion spectroscopy, have facilitated direct measurements of exoplanetary atmospheres (Samland et al. 2017; Gravity Collaboration et al. 2020).

2018AandA...616A.139G__Rao_et_al._(2009)_Instance_1

Observations of the polarized dust emission of nine low-mass protostars at 0.87 mm were obtained using the SMA (Projects 2013A-S034 and 2013B-S027, PI: A. Maury) in the compact and subcompact configuration. To increase our statistics, we also included SMA observations from three additional sources from Perseus (NGC 1333 IRAS4A and IRAS4B) and Ophiuchus (IRAS16293) observed in 2004 and 2006 (Projects 2004-142 and 2006-09-A026, PI: R. Rao; Project 2005-09-S061, PI: D.P. Marrone). The observations of NGC 1333 IRAS4A and IRAS16293 are presented in Girart et al. (2006) and Rao et al. (2009), respectively. Marrone (2006) and Marrone & Rao (2008) provide a detailed description of the SMA polarimeter system, but we provide a few details on the SMA and the polarization design below. The SMA has eight antennas. Each optical path is equipped with a quarter-wave plate (QWP), an optical element that adds a 90° phase delay between orthogonal linear polarizations and is used to convert the linear into circular polarization. The antennas are switched between polarizations (QWP are rotated at various angles) in a coordinated temporal sequence to sample the various combinations of circular polarizations on each baseline. The 230, 345, and 400 band receivers are installed in all eight SMA antennas. Polarization can be measured in single-receiver polarization mode and in dual-receiver mode when two receivers with orthogonal linear polarizations are tuned simultaneously. In this dual-receiver mode, all correlations (the parallel-polarized RR and LL and the cross-polarized RL and LR; with R and L for right circular and left circular, respectively) are measured at the same time. Both polarization modes were used in our observations. This campaign was used to partly commission the dual-receiver full polarization mode for the SMA. A fraction of the data was lost during this period owing to issues with the correlator software. Frequent observations of various calibrators were interspersed to ensure that such issues were detected as early as possible to minimize data loss.

2020MNRAS.494.2465B__Lee,_Sode-Yome_&_Park_1991_Instance_1

Here we demonstrate that, over a fixed time interval, the planar three-body problem can be solved by means of a multilayered deep artificial neural network (ANN; e.g. see LeCun, Bengio & Hinto 2015). These networks are designed for high-quality pattern recognition by mirroring the function of our brains (McCulloch & Pitts 1943; Rosenblatt 1985) and have been successfully applied to a wide variety of pattern recognition problems in science and industry, even mastering the game of Go (Silver et al. 2016). The abundance of real-world applications of ANNs is largely a consequence of two properties: (i) an ANN is capable of closely approximating any continuous function that describes the relationship between an outcome and a set of covariates, known as the universal approximation theorem (Cybenko 1989; Hornik 1991); and (ii) once trained, an ANN has a predictable and a fixed computational burden. Together, these properties lead to the result that an ANN can be trained to provide accurate and practical solutions to Newton’s laws of motion, resulting in major improvements in computational economy (Lee, Sode-Yome & Park 1991) relative to modern technologies. Our proof-of-principle method shows that an ANN can accurately match the results of converged solutions found using the arbitrary precision numerical integrator that, for computationally challenging scenarios, e.g. during multiple close encounters, can offer numerical solutions at a fraction of the time cost and CO2 expense. We demonstrate the importance of training an ANN on converged solutions. This enables the trained ANN to accurately predict particle locations even when a conventional ‘double-precision’ numerical integrator fails dramatically. By training an ANN that can accurately compute particle trajectories during close encounters, our work extends previous work training neural networks on an n-body-type problem (e.g. Quito, Monterola & Saloma 2001; Battaglia et al. 2016). Our findings also add to the growing body of literature that supports machine learning technologies being developed to enrich the assessment of chaotic systems (Pathak et al. 2018; Stinis 2019) and providing alternative approaches to classical numerical solvers more broadly (Hennig, Osborne & Girolami 2015).

2022ApJ...926..208Y__Zheng_&_Hu_2018_Instance_1

Magnetic flux ropes are frequently observed magnetic structures with helical magnetic field lines and a strong core field. In spacecraft observations, flux ropes are often identified from bipolar variations in one magnetic field component and the enhancement of magnetic strength at the center. There are various observations of flux ropes in the magnetosphere (e.g., Khurana et al. 1995; Slavin 2003; Yang et al. 2013; Poh et al. 2019; Sun et al. 2019), boundary of magnetosphere (e.g., Rijnbeek et al. 1984; Kawano & Russell 1997; Fear et al. 2008, 2009; Akhavan-Tafti et al. 2018; Hwang et al. 2018; Yao et al. 2020), and solar wind (e.g., Zheng & Hu 2018; Blanco-Cano et al. 2019; Bai et al. 2020). Flux ropes are also observed in the magnetospheres of Mercury (Zhong et al. 2020a), Mars (Briggs et al. 2011), Jupiter (Sarkango et al. 2021), and Saturn (Jasinski et al. 2016). Flux ropes are widely believed to result from magnetic reconnection. In earlier studies, some flux ropes near the magnetopause are referred to as flux transfer events (FTEs; Russell & Elphic 1978), which allow plasma transportation across the magnetopause. Various mechanisms for FTE generation have been proposed, such as single X-line reconnection (Scholer 1988; Southwood et al. 1988) and multiple X-line reconnection (Lee & Fu 1985). Recent simulations show that reconnection can generate not only fluid-scale flux ropes but also those with ion or even electron scales (Daughton et al. 2011; Hoilijoki et al. 2019; Lu et al. 2020). These kinetic-scale flux ropes may interact with one another to form larger-scale flux ropes (Daughton et al. 2011), which could be manifested in spacecraft observations as entangled flux ropes (Wang et al. 2017; Øieroset et al. 2019; Qi et al. 2020). There are also observations of ion-scale flux ropes near the ion/electron diffusion region (Wang et al. 2016; Hwang et al. 2018; Poh et al. 2019; Dong et al. 2020). The flux ropes can change in size while moving through convection, often expanding (Dong et al. 2017; Akhavan-Tafti et al. 2018, 2019) and sometimes contracting (Hasegawa et al. 2016). Eastwood et al. (2012) investigated that the flux ropes in the day side and in the distant-tail magnetopause have similar orientations and comparable magnetic flux content, indicating that the flux ropes may be in quasi-equilibrium as they are convected tailwards along the magnetopause. Moreover, ion-scale flux ropes are believed to be responsible for exciting waves (Huang et al. 2016; Wang et al. 2019) or accelerating particles (Zhu et al. 2019; Zhong et al. 2020b), implying that they are actively involved in magnetospheric physics.

2022ApJ...934..145I__from_1995_Instance_1

The projected position angle (PA) of the jet gradually rotates counterclockwise with increasing distance from the jet base. In the 2.3 GHz VLBA image the PA is 50° ± 5° east of north at about 20 mas from the core and the jet bends toward the north–south direction closer to the core, consistently with the jet orientation in the nonsimultaneous 1.6 GHz image at 5–10 mas from the core (Shen et al. 1999). At 8.7 GHz we find a PA of 25° ± 5° at about 5 mas from the core, consistent with the archival VLBA monitoring results at 15 GHz, showing a persistent jet orientation at a PA of about 30° in the epochs from 1995 to 2013 at angular scales ∼5 mas (Kellermann et al. 1998; Pushkarev et al. 2017). The same MOJAVE observations hint at more variability of the jet PA on the smallest resolved scales ∼1 mas. Observations by Shen et al. (2002) with the VLBA between 1994 and 2000 across four frequencies (5, 12, 15, and 43 GHz) showed a consistent PA orientation of 30° at 5 GHz with a clockwise shift of about 51°–67° at 43 GHz. At 86 GHz a possible bent jet structure is seen, with the inner jet oriented with a PA of about −40° less than 0.3 mas from the core, and an apparent transition to a northeast direction further out. The −40° PA with respect to the core is consistent with the PA of the single jet component located ∼400 μas from the core, imaged from 2018 April GMVA observations (see Figure 4 of Issaoun et al. 2021). 2018 images at 86 GHz do not indicate jet bending. At 230 GHz, the component C1 is located at a PA of −45° and C2 at a PA of −35° with respect to the core C0. This morphology can potentially be explained by a helical structure in the jet (Conway & Murphy 1993; Steffen et al. 1995). Such a helical jet structure can be caused by an orbiting lower-mass secondary black hole around a stable primary central black hole, as also proposed for 4C 73.18 (Roos et al. 1993) and OJ 287 (e.g., Dey et al. 2021; Gómez et al. 2022), precession caused by a wobbling disk (Britzen et al. 2018), or a large-scale accretion flow that is tilted with respect to the black hole spin axis (e.g., as in M81; Martí-Vidal et al. 2011).

2021AandA...656A..16C__Bruno_&_Carbone_2013_Instance_2

Investigations of the turbulent nature of solar wind fluctuations have been ongoing for more than half a century (see, e.g., Bruno & Carbone 2016). Advances have been made consistently thanks to the increasingly accurate measurements of several dedicated space mission as well as to the enormous improvement of numerical calculation, new detailed models and theoretical frameworks, and the development of specific data analysis techniques. Nevertheless, the extremely complex nature of the system and the coexistence of multiple actors, scales, and dynamical regimes have led to a number of questions that remain open (Viall & Borovsky 2020). Among these, the very nature of the turbulent cascade of the solar wind flow and its relationship with the small-scale processes still need to be described in full (Tu & Marsch 1995; Bruno & Carbone 2013; Matthaeus & Velli 2011; Chen 2016). Magnetic field fluctuations have been characterized with great detail at magnetohydrodynamic and kinetic scales, for example, through spectral and high-order moments analysis (Tu & Marsch 1995; Bruno & Carbone 2013). The anisotropic nature of magnetic turbulence has also been addressed, and is still being debated, due to the limited access to three-dimensional measurements in space (see, e.g., Horbury et al. 2008, 2012; Sorriso-Valvo et al. 2010; Yordanova et al. 2015; Verdini et al. 2018; Telloni et al. 2019a; Oughton & Matthaeus 2020). Velocity fluctuations have been studied thoroughly (see, e.g., Sorriso-Valvo et al. 1999; Bruno & Carbone 2013), although the kinetic scales still remain quite unexplored for instrumental limitations, most notably in the sampling time resolution. Both the velocity and magnetic field show highly variable turbulence properties, with well developed spectra, strong intermittency (Sorriso-Valvo et al. 1999), anisotropy, and linear third-order moments scaling (Sorriso-Valvo et al. 2007; Carbone et al. 2011). The level of Alfvénic fluctuations (mostly but not exclusively found in fast streams, see e.g., D’Amicis et al. 2011; Bruno et al. 2019) are believed to be associated with the state of the turbulence. In particular, solar wind samples containing more Alfvénic fluctuations are typically associated with less developed turbulence, as inferred from both shallower spectra and reduced intermittency (see Bruno & Carbone 2013, and references therein). This is consistent with the expectation that uncorrelated Alfvénic fluctuations contribute to reduce the nonlinear cascade by sweeping away the interacting structures (Dobrowolny et al. 1980), as also confirmed by the observed anticorrelation between the turbulent energy cascade rate and the cross-helicity (Smith et al. 2009; Marino et al. 2011a,b).

2021AandA...654A.124W__Gao_et_al._2015_Instance_1

In view of the ultrahigh mass of the merger product of around 2.5 M⊙, it is undoubtedly necessary and important to further test the existence of post-merger NSs (Gao et al. 2016; Li et al. 2016, 2017; Ai et al. 2018; Zhu et al. 2018; Sarin et al. 2020; Beniamini & Lu 2021), which can provide a robust constraint on the equation of state of the NS matter and then improve our understanding of the low-energy feature of strong interaction. Besides the GRB 170817A/AT 2017gfo event, searches for possible kilonova emission have already been carried out in the afterglows of many SGRBs since 2013 (Berger et al. 2013; Tanvir et al. 2013; Yang et al. 2015; Jin et al. 2015, 2016, 2018, 2020; Gao et al. 2015, 2017; Kasliwal et al. 2017). Among the SGRBs with a kilonova candidate, GRB 160821B has one of the lowest redshifts, of namely z = 0.162. From its optical/near-infrared(NIR) afterglow, an obvious excess was found. Because of its close distance, the kilonova emission associated with GRB 160821B is in principle detectable and can provide a natural explanation for the observed optical/NIR excess (Lamb et al. 2019; Troja et al. 2019). In view of its luminosity, which is lower than that of AT 2017gfo, the kilonova after GRB 160821B can in principle be modeled with a pure radioactive power. However, it could still be necessary to mention that a significant internal plateau appeared in the early X-ray afterglow during the first few hundred seconds (see the insert in Fig. 1), which indicated that a post-merger NS also exists in this event. According to these observations, Ma et al. (2021) suggested that the post-merger NS could collapse into a black hole and then the subsequent kilonova could be powered by the accretion onto the black hole. Nevertheless, alternatively, as suggested by Yu et al. (2018), the steep decay after the internal plateau may not represent the collapse of the NS, but may simply be caused by the suppression of the magnetic dipole radiation of the NS. In this case, the spin-down of the NS of relatively low luminosity can still power the kilonova emission, which can be generally called a mergernovae (Yu et al. 2013). This scenario can provide a natural explanation for the AT 2017gfo emission. Therefore, in our opinion, this situation could also appear in the case of GRB 160821B. This paper is devoted to testing whether or not there is a nonthermal emission component arising from the interaction between the NS wind and the merger ejecta, as mentioned above for AT 2017gfo.

2020ApJ...894..107I__Kastner_et_al._1994_Instance_1

AFGL 2136 IRS 1 (also referred to as CRL 2136, G17.64+0.16, and IRAS 18196−1331) is a luminous (1.0 × 105 L; Lumsden et al. 2013), high-mass (45 ± 10 M; Maud et al. 2019) protostar that is believed to be in the latter stages of its evolution due to a variety of observed characteristics (Boonman & van Dishoeck 2003; Maud et al. 2018 and references therein). It is located at a distance of 2.2 kpc away from the Sun (Urquhart et al. 2014), and has been extensively observed from centimeter to micron wavelengths, at low and high angular resolution, and low and high spectral resolution. The myriad observations paint a picture where a single, isolated massive protostar is driving a wide-angle bipolar outflow through its natal cloud. The large scale outflow is observed in CO emission at millimeter wavelengths, with both the red and blue lobes being about 100″ in extent (Kastner et al. 1994; Maud et al. 2018). Closer to the central source (2″–10″), the outflow cavity walls are seen in scattered light at near-infrared wavelengths (Kastner et al. 1992; Murakawa et al. 2008; Maud et al. 2018). The cool molecular envelope exhibits ice and dust absorption bands (Willner et al. 1982; Keane et al. 2001b; Dartois et al. 2002; Gibb et al. 2004), as well as molecular emission at millimeter wavelengths (van der Tak et al. 2000a, 2000b), but a much warmer component is also inferred from several different molecules seen in absorption in the near- to mid-infrared (Mitchell et al. 1990; Lahuis & van Dishoeck 2000; Keane et al. 2001a; Boonman et al. 2003; Boonman & van Dishoeck 2003; Goto et al. 2013, 2019; Indriolo et al. 2013a). The presence of a dust disk on small spatial scales was suggested by near-infrared polarization imaging (Minchin et al. 1991; Murakawa et al. 2008) and by mid-infrared interferometric observations (de Wit et al. 2011; Boley et al. 2013). A compact source was marginally resolved at centimeter wavelengths, along with a cluster of nearby 22 GHz H2O masers (Menten & van der Tak 2004), but only with the recent ALMA 1.3 mm continuum observations has the 93 × 71 mas dust disk been fully resolved (Maud et al. 2019). Thermal line emission at 232.687 GHz from the H2O ν2 = 1–1, 55,0–64,3 transition has the same spatial extent as the dust emission, and the H2O gas velocities indicate Keplerian rotation within the disk (Maud et al. 2019). It is ideal that the reader has a clear picture of the AFGL 2136 region in mind to best understand the discussion throughout this paper. In particular, Figure 10 of Maud et al. (2018) provides an up-to-date schematic diagram of the AFGL 2136 region, and Figures 1 and 2 of Maud et al. (2019) present the compact disk observed in dust and gas emission, respectively.

2021AandA...650L...4B__DeForest_et_al._2016_Instance_1

As Parker Solar Probe (PSP) descends deeper into the solar corona on its succeeding orbits, its measurements reveal features of the heliospheric plasma that will significantly increase our fundamental understanding of the workings of the solar corona, the origins of the solar wind, and the behavior of heliospheric energetic particle populations (Fox et al. 2016). In the first several orbits, PSP approached progressively nearer to the range of altitudes at which the accelerating solar wind speed exceeds the Alfvén speed. This critical zone is not a simple surface, but a more irregularly defined region above which (incompressive) magnetohydrodynamic signals, such as Alfvén waves, can no longer return to the lower altitude corona. In this very region, the analysis of heliospheric imaging (HI; DeForest et al. 2016) has described a transition from striated images that appear to be highly collimated due to a structured magnetic field and lower plasma beta to higher altitude images that appeared to be more disordered and isotropic, a condition described as “flocculation.” A number of interesting features have been reported from observations in the region, including periods of near-corotation and, notably, the appearance of magnetic reversals or “switchbacks” (Bale et al. 2019) and accompanying plasma jets (Kasper et al. 2019). Switchbacks have received considerable attention, with a focus on understanding how various plasma properties respond in and near them (Bale et al. 2019; Kasper et al. 2019; Dudok de Wit et al. 2020; McManus et al. 2020; Mozer et al. 2020; Whittlesey et al. 2019). Their origin is also a subject of active discussion, with ideas ranging from distant generation in the lower corona via interchange reconnection (Axford et al. 1999; Fisk & Kasper 2020; Zank et al. 2020) to a roll-up of the magnetic field by vortices in shear-driven turbulence (Ruffolo et al. 2020). Here we examine another important set of PSP observations and their behavior near switchbacks, namely the behavior of energetic particles as measured on PSP by the integrated Science Investigation of the Sun (IS⊙IS) instrument suite (McComas et al. 2016, 2019).

2018AandA...609A..28B__Hansen_&_Milosavljević_2003_Instance_1

A number of works have tried to explain this conundrum with different ideas, which follow one of three possibilities. When Genzel et al. (1996) first discovered missing RGB stars, they proposed that this might be due to stellar collisions depleting giant stars in the innermost parts through the high stellar densities that are reached near the SMBH. Later, Davies et al. (1998), Alexander (1999), Bailey & Davies (1999), and Dale et al. (2009) addressed this idea in detail and came to the conclusion that it can only explain the absence of the brightest and most extended giant stars. A different suggested possibility to explain the missing stars is that our GC does not only have one, but a binary of two massive black holes. This hypothesised binary could indeed carve a core into the stellar distribution through three-body interactions, as shown by a number of authors (Baumgardt et al. 2006; Portegies Zwart et al. 2006; Matsubayashi et al. 2007; Löckmann & Baumgardt 2008; Gualandris & Merritt 2012). Nonetheless, the mass of the secondary needs to be of the order of \hbox{${\sim}10^5~M_{\odot}$} ~105M⊙ to explain the observed core. Such a massive secondary black hole would require the Milky Way to have experienced a major merger relatively recently, which is excluded by observations (see Hansen & Milosavljević 2003; Yu & Tremaine 2003; Chen & Liu 2013). Moreover, the existence of such a massive secondary black hole is largely ruled out from a number of other considerations, for instance, constraints on the proper motion of Sgr A* from radio interferometry (see Gualandris & Merritt 2009). A number of inspiraling smaller-mass black holes can also create a shallow stellar density profile in the centre, which would relax the major merger requirement, as has been shown by Mastrobuono-Battisti et al. (2014). It has also been put forward that a star cluster falling towards the GC could increase the density profile outside of 10′′ , so that within this distance the profile would be like a core (Kim & Morris 2003; Ernst et al. 2009; Antonini et al. 2012; Antonini 2014). However, mass segregation would rebuild a steeper profile in as fast as a quarter of the relaxation time (as shown by Preto & Amaro-Seoane 2010; Amaro-Seoane & Preto 2011). This requirement would hence need a steady inflow of clusters to maintain a weak cusp profile in the centre. Finally, Merritt (2010) and Antonini (2014) found that if the nuclear cluster in the GC formed with an extended enough initial core profile, the current stellar distribution would still not be dynamically relaxed. While this solution is possible, it requires fine-tuning in the initial conditions to produce the density distribution seen in the GC. Amaro-Seoane & Chen (2014) proposed that the discs of young stars observed at the GC (Paumard et al. 2006) are connected to the missing bright giants: the precursor gaseous discmust have gone through a fragmentation phase that produced dense enough clumps to ensure an efficient removal of the outer layers of the giants through collisions, rendering them invisible to observations. Their degenerate cores would nonetheless populate the same area of phase space where the missing bright giants should be. Kieffer & Bogdanović (2016) recently showed that in order to be viable, this scenario requires the total mass of the fragmenting disc to have been several orders of magnitude higher than that of the early-type stars in the stellar discs in the GC.

2021MNRAS.504..444C__Steiner_&_McClintock_2012_Instance_1

After a first part of ballistic, high-speed motion, the deceleration of RK1 was rather abrupt, which is something not observed in the majority of discrete ejecta from BH XRBs (e.g Mirabel & Rodríguez 1994; Fender et al. 1999; Miller-Jones et al. 2012). This scenario is consistent with a jet that travels first at constant speed in a low-density region of the ISM, which constitutes a large scale cavity around the system, before hitting the higher density wall of the cavity itself, as already proposed in Hao & Zhang (2009). Radio emission at late times would be produced by the external shocks between the plasma blob and the ISM cavity wall, in analogy with GRB afterglows (Wang et al. 2003). Those cavities have been suggested to exist at ∼pc scales at least for XTE J1550–564 and H1743–322 (Hao & Zhang 2009; Steiner & McClintock 2012; Steiner, McClintock & Reid 2012; Migliori et al. 2017). We obtained the best modelling for the RK1 motion by a combination of a linear motion and a Sedov phase (see Section 3.3), achieved at late times due to the jet sweeping up ISM material on its path, in a similar way as Miller-Jones et al. (2011), which derived it from Wang et al. (2003). We therefore suggest that MAXI J1348–630 is located in a similar cavity, possibly carved by previous jet activity, or resulting from the action of accretion disc winds. However, so far such winds have not been detected for MAXI J1348–630. If we take our estimate of α0 ∼ 25 arcsec, the upper limit on the inclination angle of the discrete ejection of θ1 ∼ 44○ and the lowest acceptable distance of 1.6 kpc, we obtain that RK1 travelled at least ∼0.3 pc before reaching the angular distance α0, which is consistent with what has been observed for other discrete ejections (Corbel et al. 2000; Gallo et al. 2004) and could be taken as a rough lower limit on the size of the cavity.7 As argued by Hao & Zhang (2009), the presence of an underdense cavity could be a common characteristic of BH XRBs environments (Heinz 2002), and its existence could be strictly required for the jet to travel such a long distance (Hao & Zhang 2009).

2018ApJ...852L...1Z__Metzger_2017_Instance_1

The short gamma-ray burst (GRB) 170817A was observed by the Fermi- Gamma-ray Burst Monitor (GBM; Goldstein et al. 2017). The fact that it was not detected by the Insight Hard X-ray Modulation Telescope (Li et al. 2017) suggests that the burst had a very weak fluence and a soft spectrum. The burst was highly noticeable because of its connection to gravitational-wave (GW) event GW170817, which was detected by the Laser Interferometer Gravitational-wave Observatory, about 1.7 s before the GBM was triggered (Abbott et al. 2017a). To understand the event, it is crucial that we obtain all of the object’s physical properties. Compared with other short GRBs, the luminosity of GRB 170817A was extremely weak, thus suggesting that the jet was off-axis to the line of sight (Albert et al. 2017; Burgess et al. 2017; Fong et al. 2017; Granot et al. 2017a; He et al. 2017; Jin et al. 2017; Margutti et al. 2017; Metzger 2017; Troja et al. 2017; Wang et al. 2017; Xiao et al. 2017). The GW fitting parameters suggest an angle of less than 28° (Abbott et al. 2017a). Given that no prompt X-rays were detected, a number of predictions have been made for the off-axis angle. From observations by the and telescopes, Evans et al. (2017) suggested that the viewing angle was . Constraints from deep Chandra observations suggest that it was greater than 23° (Haggard et al. 2017), whereas constraints from radio-frequency observations of a relativistic jet (Alexander et al. 2017) suggested it was . On the basis of the upper limit from the Atacama Large Millimeter/submillimeter Array (ALMA) and Giant Metrewave Radio Telescope (GMRT) at radio bands, Kim et al. (2017) found the angle to be 41° (or 17°) but did not rule out other values. The modeling for several different bands suggested that the viewing angle was (Ioka & Nakamura 2017), (Granot et al. 2017a), or (Guidorzi et al. 2017). The versus plot in Pozanenko et al. (2017) shows that GRB 170817A belongs to neither the long nor short GRB groups, thus supporting the idea that it was triggered by an off-axis jet. However, the exact off-axis angle is still hard to predict. If it was derived from the relativistic beaming effect, it would have degenerated with the jet’s Lorentz factor; if it was derived from the parameter fitting of the GW signal, it would have degenerated with the distance.

2017ApJ...845...86E__Soler_&_Terradas_2015_Instance_3

Among the suggestedmechanisms responsible for the strong damping of the coronal loop oscillations (e.g., Ruderman & Roberts 2002; Ofman 2005, 2009; Morton & Erdélyi 2009) resonant absorption of the MHD waves, which was established first by Ionson (1978), is a strong candidate. Several works developed this theory (e.g., Davila 1987; Sakurai et al. 1991a, 1991b; Goossens et al. 1995; Goossens & Ruderman 1995; Erdélyi 1997; Cally & Andries 2010). The necessary condition for the resonant absorption is a continuum of Alfvén or slow frequency across the loop (Ionson 1978; Hollweg 1984, 1987; Davila 1987; Sakurai et al. 1991a). Resonant absorption occurs when the frequency of the global MHD mode matches at least with one of the frequencies of the background Alfvén or slow continuum at a location called he resonance point. As a result, the energy of the global MHD mode transfers to the local Alfvén modes in a layer around the resonance point, named the resonance layer (Lee & Roberts 1986; see also Goossens et al. 2013; Soler & Terradas 2015). In the absence of dissipation mechanisms, the amplitude of the oscillations diverges at the resonance point. Dissipation is important in the resonance layer, where the oscillations make large gradients. The background Alfvén or slow continuum can be due to the variation of the plasma density (e.g., Davila 1987; Ofman et al. 1994; Ruderman & Roberts 2002; Terradas et al. 2006; Soler & Terradas 2015), twisted magnetic field (Ebrahimi & Karami 2016), or both of them together (Karami & Bahari 2010; Giagkiozis et al. 2016). There are a variety of theoretical works related to the damping of the coronal loop oscillations based on the theory of resonant absorption of MHD waves (e.g., Goossens et al. 2002, 2009; Ruderman & Roberts 2002; Van Doorsselaere et al. 2004; Andries et al. 2005; Terradas et al. 2006; Karami et al. 2009; Karami & Bahari 2010; Soler et al. 2013; Soler & Terradas 2015; Ebrahimi & Karami 2016; Giagkiozis et al. 2016; Jung Yu & Van Doorsselaere 2016). For a good review about the theory of resonant absorption, see also Goossens et al. (2011).

2018ApJ...855...48Q__Falgarone_&_Passot_2003_Instance_1

The dust condensations could not arise from a thermal Jeans fragmentation process. If that is the case, with a density of 104–105 cm−3 for the surrounding medium, one may expect the mass of the condensations on the order of 20–50 M⊙ (the Jeans mass at 105–104 cm−3) and the nearest separations between the condensations around 0.2–0.5 pc (the Jeans length at 105–104 cm−3); both are clearly inconsistent with the observations. Alternatively, small dense structures can be temporary density fluctuations frequently created and destroyed by supersonic turbulence (e.g., Elmegreen 1999; Biskamp 2003; Falgarone & Passot 2003). However, σnth is found to be subsonic or at most transonic. Goicoechea et al. (2016) also found that there is only a gentle level of turbulence in the Bar. So the turbulence does not seem to be strong enough to produce the condensations. Another force that could potentially compress the cloud and produce high-density structures is a high-pressure wave from the expansion of the H ii region. Goicoechea et al. (2016) detected a fragmented ridge of high-density substructures at the molecular cloud surface and three periodic emission maxima in HCO+ (4–3) from the cloud edge to the interior of the Bar, providing evidence that a high-pressure wave has compressed the cloud surface and moved into the cloud to a distance of ∼15″ from the dissociation front. The dust condensations are also located within a distance of 15″ from the dissociation front, and thus are very likely over-dense structures created as the compressive wave passed by. The complex clumpy appearance of the condensations is probably related to the front instability of the compressive wave (Goicoechea et al. 2016), or an instability developed across different layers of the Bar (e.g., the thin-shell instability, García-Segura & Franco 1996). The velocity structure of dense gas around the dust condensations may provide insights into the feasibility of this scenario. Figure 10 shows the intensity-weighted velocity map of the H2CS (71,7–61,6) emission. A velocity gradient in a northwest–southeast direction (i.e., along the direction of the propagation of the compressive wave) is seen in the map, and is consistent with the scenario that the gas is being compressed by a high-pressure wave.

2021AandA...652A.124N__Wiśniewska_et_al._(2016)_Instance_1

Figure 9 presents Fourier power spectrum for wave period P versus height. The initial pulse has a Gaussian spectrum of wave number k which results in a spectrum of period P. The steepening of the magnetoacoustic waves results from the growing wave amplitude with height. Hence, waves with shorter wavelengths and wave periods are present for higher y-values in their Fourier spectra. For the pulse launched from y = y0 = 0 Mm (top), the main wave period of the downward-propagating waves becomes approximately the same for all values of y 0 Mm and is equal to about 250 s. Higher up, however, the period P decays with increasing y, and attains values close to 200 s. As a result of the cut-off only short-period waves can propagate upwards while long-period waves become evanescent. Hence, the relative contribution of long P waves weakens with increasing y. We note that some of our data fits the observational findings of Wiśniewska et al. (2016), represented by diamonds over-plotted on the power spectra, and Kayshap et al. (2018), denoted by dots. The agreement of the theory with the observational data indicates that the results can be used to determine the background structure of the solar atmosphere and confirms that wave generation by the solar granulation in the partially ionized plasma dominates the behavior of the waves. For a pulse at the bottom of the photosphere (y = y0 = 0 Mm, Fig. 9 (top)), a jump in the dominant wave period is observed close to the height y = 1.5 Mm, which reaches a magnitude of 300 s. The signal launched from the bottom of the photosphere with the main period P = 250 s thus reaches the corona with P = 300 s. The wave periods in the photosphere are lower than Pac in this region, because max Pac = 240 s at y = 0.5 Mm, which means that magnetoacoustic waves are evanescent. However, in the corona above y = 2.3 Mm the acoustic cut-off period reaches values larger than P = 300 s. However, this is not the case for a pulse launched at a somewhat greater height, in the middle of the photosphere, namely for y = y0 = 0.25 Mm (see Fig. 9 (bottom)). Moreover, in this case the layers below the photosphere (y  0) oscillate with a dominant wave period of 225 s. In the photosphere, this wave period is also dominant and this time it increases slightly with height in the photosphere. Moreover, in this case there is a second dominant period, namely 250 s. In fact, this wave period is also the dominant one in the upper chromosphere and low corona in this case.

2017MNRAS.465..492M__Taverna_et_al._2015_Instance_1

Given the quite strong surface magnetic field of the M7, thermal radiation is expected to be polarized, either if emission is from a bare surface or from an atmosphere (see Turolla et al. 2004; Potekhin 2014). The polarization properties are quite different in the two cases, although there are still uncertainties, especially at optical/ultraviolet (UV) wavelengths. One of the first predictions of quantum electrodynamics (QED), even before it was properly formulated, was vacuum birefringence, and, in particular, that a strong magnetic field affects the propagation of light through it (Heisenberg & Euler 1936; Weisskopf 1936). In thermally emitting INSs, radiation comes from a region comparable with the entire star surface, over which the magnetic field direction changes substantially. In the absence of QED vacuum polarization effects, this would produce a drastic depolarization of the radiation collected at infinity (Heyl, Shaviv & Lloyd 2003, see also Taverna et al. 2015; González Caniulef et al. 2016 and references therein). Vacuum birefringence dramatically increases the linear polarization of the observed radiation, from a level of a few  per cent up to even ∼100 per cent, depending on the viewing geometry and the surface emission mechanism (Heyl & Shaviv 2000, 2002; Heyl et al. 2003; Taverna et al. 2015; González Caniulef et al. 2016). Detecting polarization in the thermal emission from the surface of an INS will be therefore extremely valuable. First, and independently on the physical conditions of the emitting region, the detection of a large degree of linear polarization in the signal would constitute the observational evidence of QED vacuum polarization effects in the strong-field regime. Secondly, the polarization observables can be compared with emission models and help to uncover the physical conditions of INS surfaces and atmospheres, ideally complementing spectral observations (Taverna et al. 2014; González Caniulef et al. 2016).

2019AandA...622A.146M__Arribas_et_al._(2014)_Instance_2

Previous works (e.g. Holt et al. 2011; Arribas et al. 2014; Villar Martín et al. 2014, 2015) have found very high reddening and densities associated with ionised outflows in local objects (e.g. Hα/Hβ ∼ 4.91 and ne ≳ 1000 cm−3, Villar Martín et al. 2014). Concerning the reddening, although we find that the outflowing gas is generally less affected by dust extinction than the disc, the median value of the total distribution is significantly affected by dust (Hα/Hβ ∼ 4.16), with tails up to Hα/Hβ ≳ 6. Similarly, the outflow density of MAGNUM galaxies is higher than the values in the disc gas, but appears to be far lower than the values found by these authors. This could stem from the fact that the galaxies studied by Holt et al. (2011), Arribas et al. (2014) are local luminous or ultra-luminous infrared galaxies (U/LIRGs), and those of Villar Martín et al. (2014, 2015) are highly obscured Seyfert 2, thus sampling sources that are more gas and dust rich compared to our sample. However, our values are also lower than the outflow densities found in Perna et al. (2017) (ne ∼ 1200 cm−3), who targeted optically selected AGNs from the SDSS, and Förster Schreiber et al. (2018b), who presented a census of ionised gas outflows in high-z AGN with the KMOS3D survey (ne ∼ 1000 cm−3). A possible explanation could be related to the high quality of our MUSE data, which also allows us to detect the faint [S II] emission associated with lower density regions. If we calculate the median densities of the disc and outflow components, weighting for the [S II] line flux, we obtain higher values (ne ∼ 170 cm−3 and ne ∼ 815 cm−3, for disc and outflow, respectively). This shows that previous outflow density values from the literature could be biased towards higher ne because they are based only on the most luminous outflowing regions, characterised by a higher S/N. This could also mean that outflows at high-z could be far more extended than the values we observe.

2020AandA...641A.155V__Gómez-Guijarro_et_al._2019_Instance_1

The scenario presented above has been formulated in various flavors to individually explain several of the properties reported here. The main addition of this work, namely the excitation of CO in distant main-sequence and starburst galaxies, fits in the general picture that we sketched. The ensemble of properties and correlations that we reported here can be also used to revisit the definition of what a starburst is. A standard operational classification is based on the distance from the observed empirical M⋆-SFR correlation, the main sequence. This proved to be a useful distinction and an excellent predictor of several trends (e.g., Sargent et al. 2014), but recent results, including our present and previous analysis (Puglisi et al. 2019), show that the demarcation between starburst and main-sequence galaxies is more blurred that we previously considered. We do detect starburst-like behaviors in galaxies on the main sequence (Elbaz et al. 2018), likely linked to the existence of transitional objects (Popping et al. 2017; Barro et al. 2017b; Gómez-Guijarro et al. 2019; Puglisi et al. 2019, and in prep. to limit the references to recent works based on submillimeter observations). Such transition might well imply an imminent increase of the SFR, driving the object in the realm of starbursts (e.g., Barro et al. 2017b), or its cessation, bringing the system back onto or even below the main sequence (Gómez-Guijarro et al. 2019; Puglisi et al. 2019), with the CO properties potentially able to distinguish between these two scenarios. Regardless of these transitional objects, a definition of starburst based on ΣSFR, rather than ΔMS, would naturally better account for the observed molecular gas excitation properties, dust temperatures and opacities, or SFE (see also Elbaz et al. 2011; Rujopakarn et al. 2011; Jiménez-Andrade et al. 2018; Tacconi et al. 2020). As an example, in Fig. 8 we show the mean SLED of the subsample of galaxies with both CO (2 − 1) and CO (5 − 4) coverage, split at its median ΣSFR. While only tentative at this stage, this suggests a trend of increasing CO excitation with ΣSFR, consistently with Fig. 7 and what mentioned above.

2022MNRAS.512.2854P__Pétri,_Heyvaerts_&_Bonazzola_2002_Instance_1

The aforementioned fluid description offers a good starting point to understand the global electric circuit made of charge and current densities. However, it neglects some fundamental kinetic aspects required to self-consistently include single particle acceleration as well as radiation feedback. As kinetic simulations are much more demanding than fluid models, this approach was only scarcely investigated in the last century. Let us mention Krause-Polstorff & Michel (1985) who computed axisymmetric dead pulsar magnetospheres called electrospheres. Due to the axisymmetry of the problem they used rings of charges instead of point particles. Later with the advent on computational power, Smith, Michel & Thacker (2001) showed with slightly more sophisticated simulations that a fully field magnetosphere is unstable and collapse to an electrosphere. The first full three-dimensional electrosphere was constructed by McDonald & Shearer (2009), using an electromagnetic Particle in Cell (PIC) code. They neglect pair creation and therefore did not add any particle injection process. Eventually Philippov & Spitkovsky (2014) computed the first two-dimensional axisymmetric pulsar magnetosphere for an aligned rotator by permanently injecting particle supposed to be released from the surface, avoiding to end to an electrosphere configuration (Pétri, Heyvaerts & Bonazzola 2002). Depending on the volume injection rate, they were able to find any equilibrium between the force-free and the fully charge separated state. Chen & Beloborodov (2014) improved this model by adding a prescription for the pair creation, putting a threshold on the lepton Lorentz factor. Following the same lines, Cerutti et al. (2015) assumed particle injection only from the vicinity of the stellar surface. Belyaev (2015) injected particles from regions where a parallel electric field exists. The first full three-dimensional PIC simulations of a pulsar magnetosphere were performed by Philippov, Spitkovsky & Cerutti (2015b). For an aligned rotator Philippov et al. (2015a) also included general-relativistic corrections with frame-dragging. Soon after some observational signature predictions were added to compute light curves and spectra emanating from curvature and or synchrotron radiation like for instance Cerutti, Philippov & Spitkovsky (2016b) who then included polarization (Cerutti, Mortier & Philippov 2016a). This PIC simulations were then extended to the striped wind well outside the light-cylinder to study its dissipation (Cerutti & Philippov 2017; Cerutti, Philippov & Dubus 2020). The oblique magnetosphere with radiation and general-relativistic correction was eventually computed by Philippov & Spitkovsky (2018). Several other groups performed similar simulations like Brambilla et al. (2018) or Kalapotharakos et al. (2017, 2018) who tried to explicitly connect their simulation results to gamma-ray observations. Alternatively, more simply test particle trajectories can be explored within a fluid code (see for instance Brambilla et al. 2015).

2021AandA...646L...9M__Quan_&_Herbst_(2007)_Instance_1

The chemistry of C4H3N isomers in cold molecular clouds was discussed by Balucani et al. (2000), and more specifically by Balucani et al. (2002), based on crossed molecular beam experiments and ab initio calculations. In these studies, it was pointed out that reactions of the CN radical with methyl acetylene and allene are barrier-less and exothermic when producing CH3C3N and CH2CCHCN in the methyl acetylene reaction, and CH2CCHCN and HCCCH2CN in the reaction involving allene. Indeed, the reactions of CN with CH3CCH and CH2CCH2 were measured to be rapid at low temperatures (Carty et al. 2001). This chemical scheme was implemented in a chemical model by Quan & Herbst (2007) to explain the abundance of cyanoallene in TMC-1. Later on, Abeysekera et al. (2015) measured the product branching ratios of the reaction between CN and methyl acetylene at low temperatures using a chirped-pulse uniform flow and found that HC3N is the major product, while CH3C3N accounts for 22% of the products and CH2CCHCN is not formed. These results are in contrast with those obtained from crossed molecular beam experiments (Huang et al. 1999; Balucani et al. 2000, 2002), where CH2CCHCN is observed as a product of the CN + CH3CCH reaction. Therefore, the most stable isomer, CH3C3N, can be formed in the reaction of CN and methyl acetylene, the second most stable isomer, CH2CCHCN, can be formed when CN reacts with CH2CCH2 – and perhaps also with CH3CCH, depending on whether one gives credit to the chirped-pulse uniform flow experiment or to the crossed molecular beam ones – and the least stable isomer, HCCCH2CN, can only be formed in the reaction between CN and allene. These neutral-neutral reactions involving CN are therefore likely routes to the three C4H3N isomers in cold interstellar clouds such as TMC-1, where abundant CN, CH3CCH, and probably CH2CCH2 (non-polar and thus cannot be detected at radio wavelengths) are present. Moreover, the presence of HCCCH2CN (and perhaps also CH2CCHCN) can be used as a proxy of the non-polar C3H4 isomer allene since this isomer is only formed from CH2CCH2 in the aforementioned reactions of CN.

2022MNRAS.509..619I__Różańska_et_al._2011_Instance_1

A number of sources of systematic error disproportionately affect the soft X-rays. One important example is absorption by partially ionized material around the AGN. Although the response of the absorbing gas to changes in the irradiating flux from the AGN does contribute its own time lag, this should only influence the lag-energy spectrum for Fourier frequencies lower than those of interest for our analysis (Silva, Uttley & Costantini 2016). The influence of absorption on the soft X-ray region of the spectrum (e.g. Miller, Turner & Reeves 2008; Miller et al. 2010), however, is potentially much more important and can influence the shape of the lag-energy spectrum (I19). It will therefore be prudent to select the AGN with the clearest view of the inner regions. The soft X-ray region of the reflection spectrum is also the most sensitive to modelling assumptions such as vertical disc structure (Nayakshin, Kazanas & Kallman 2000; Done & Nayakshin 2007; Różańska et al. 2011; Vincent et al. 2016). Moreover, X-ray reverberation models currently always assume that the time taken for photons to be reprocessed and reemitted in the disc atmosphere is very small compared with the light-crossing delays. This is a very good assumption for the fluorescence and scattering processes dominant for E ≳ 3 keV. However, the time taken for soft excess photons to be reprocessed will be longer, since these photons undergo enough interactions to approximately thermalize. This thermalization time is still expected to be small, but it is not yet clear if it is small enough to be neglected entirely. It is also important to note that the model we have explored here does not include a warm (kTe ∼ 0.1 keV), optically thick (τ ∼ 10–40) corona in addition to the hot corona, as is often invoked to explain the observed soft excess in AGNs (Czerny & Elvis 1987; Middleton et al. 2009; Done et al. 2012; Petrucci et al. 2018; Ursini et al. 2020). Models 1, 2, and 4 therefore effectively assume that the observed soft excess is dominated by reflection (e.g. Jiang et al. 2018; García et al. 2019). If in reality there is indeed a warm corona, its presence would make it more difficult to constrain the soft X-ray shape of the reflection spectrum (Xu et al. 2021). Since we believe our models to be most robust for E ≳ 3 keV, we ran an alternative fit ignoring E 3 keV (Model 3), and found that the 3σ error on H0 almost doubles (dH0 ≈ 60 km s−1 Mpc−1) compared to the other fits. It is therefore desirable to also include soft X-rays, but it is encouraging that constraints can even be achieved without. Ignoring soft X-rays entirely is an extreme measure. An alternative approach would be to keep the soft X-rays but trial a variety of different models, for instance with and without a warm corona. This approach would likely return uncertainties somewhere between the two extreme scenarios explored here.

2019MNRAS.489.2792Z__Zahid_et_al._2014_Instance_1

The compact blobs instead likely have a different origin. The fact that compact blobs are unresolved even at the HST resolution, that they are found at ∼1 kpc distance from the galaxy barycentre, that they have relatively small stellar masses (≲15 per cent of the underlying disc), but are actively forming stars suggests that they are star-forming regions likely originated due to disc instability and fragmentation of the galaxy disc (Bournaud et al. 2014; Mandelker et al. 2017). The in situ formation of the compact blobs is further supported by their metallicity. In fact, while the disc properties are consistent with the stellar mass–metallicity relation of z ∼ 2 star-forming galaxies (e.g. Maiolino et al. 2008; Zahid et al. 2014), compact blobs instead show metallicities inconsistent with the mass–metallicity relation (Fig. 7, bottom right panel). They have a comparable metallicity to the discs, but ∼1.5 dex lower stellar masses, so they are ∼1 dex above the mass–metallicity relation. Metallicity measurements for statistical sample of galaxies with M⋆ ∼ 108 M⊙ at z ∼ 2 are still lacking and therefore at the low-mass end we are showing an extrapolation of the mass–metallicity relation derived for galaxies with M⋆ ≳ 109 M⊙. In Fig. 7, we also show the average location of dwarf galaxies with M⋆ ∼ 108 M⊙ at z ≲ 1 (Kirby et al. 2013; Calabrò et al. 2017; Hidalgo 2017). Despite some of them may have gas-phase metallicities up to 12 + log (O/H) ∼ 8.5 (Sánchez Almeida et al. 2018), on average our compact blobs seem to be ∼0.5 dex more metal-rich than dwarf galaxies (Fig. 7). The high metallicity of compact clumps further suggests that they formed in situ, due to the gravitational collapse of pre-enriched gas in unstable regions of the galaxy disc. The young ages of the blobs reported in Fig. 7 support these conclusions. In fact, the metallicity of star-forming regions is altered in about one galactic dynamical time (≳100 Myr), increasing due to the active star formation and internal production of metals. The fact that our sample clumps with metallicity measurements have ages ≲ 50 Myr points towards the conclusion that they formed in situ from metal-rich gas, since this time-scale is too short for the gas to be self-enriched due to internal star formation (Bournaud 2016). Finally, the metallicity of our sample of clumps does not clearly correlate with their star formation rate, as indeed expected if they formed from pre-enriched material, although larger statistical samples are needed to confirm this finding.

2018AandA...616A..99K__Narang_et_al._2016_Instance_2

The high-resolution imaging observations of TR from IRIS reveal the ubiquitous presence of network jets. We have used three different IRIS observations of the quiet sun, which are located near the disk center. On the basis of careful inspection, 51 network jets are identified from three QS observations and used for further analysis. These 51 network jets are very well resolved and are not affected by the dynamics of other jets. The study is focused on the rotating motion of network jets along with the estimation of their other properties (speed, height, and lifetime). The mean speed, as predicted by statistical distributions of the speed, is 140.16 km s−1 with a standard deviation of 39.41 km s−1. The mean speed of network jets is very similar, as reported in previous works (e.g., Tian et al. 2014; Narang et al. 2016). However, in case of their lifetimes, we found a value that is almost double (105.49 s) that of the previously reported mean lifetime of the network jets (49.6 s; Tian et al. 2014). As mentioned above, we took only those network jets that are very well resolved in space and in the time; these criteria exclude short lifetime network jets. Therefore, our statistical distribution of the lifetime predicts a higher mean lifetime. The mean length of the network jets is 3.16 Mm with a standard deviation of 1.18 Mm. In the case of CH network jets, Tian et al. (2014) have reported that most of the network jets have lengths from 4.0 to 10.0 Mm. However, the mean length for QS network jets is smaller (3.53 Mm; Narang et al. 2016). So, the mean length for QS network jets from the present work is in good agreement with Narang et al. (2016). In addition, the apparent speed and length of these network jets are positively correlated, which is very similar to what has already been reported in previous works (Narang et al. 2016). Finally, we can say that these networks jets are very dynamic features of the solar TR, as revealed by their estimated properties.

2016ApJ...819...23S__Kendall_et_al._1992_Instance_1

Single-photon VUV excitation from the N2(X1 ) ground state can only populate the ungerade states. Many of these ungerade states can be coupled by spin–orbit or vibronic interactions. Hence, only the ungerade states of N2 are calculated and presented. Because of the potential diffuse nature of the excited states, their wavefunctions were calculated using a large basis set composed by the aug-cc-pVQZ quality, which is augmented by 3s and 2p diffuse Gaussian-type orbitals (GTOs; Dunning 1989; Kendall et al. 1992; Woon & Dunning 1995). In addition, the expected high density of the electronic states in this energy range should lead to the mixing of their electronic wavefunctions. Therefore, a multiconfigurational approach is adopted where the electronic computations were carried out using the Complete Active Space Self Consistent Field (CASSCF; Knowles & Werner 1985; Werner & Knowles 1985) approach followed by the internally contracted Multi Reference Configuration Interaction (MRCI; Knowles & Werner 1988; Werner & Knowles 1988) technique as implemented in MOLPRO (Werner et al. 2012). We followed the procedure established in Spelsberg & Meyer (2001), Ndome et al. (2008), and Hochlaf et al. (2010a, 2010b). Briefly, the CASSCF active space is constructed by the valence molecular orbitals (MOs) of N2 increased by one sg and one pg MOs. This allows better relaxation of the wavefunctions of the N2 electronic states whose configurations differ in their s and p orbital occupations as those of interest at present. In MRCI, all CASSCF configurations were taken into account as reference. Finally, the CASSCF wavefunctions were used to evaluate the spin–orbit matrix elements. These spin–orbit computations were performed in Cartesian coordinates, where the CASSCF wavefunctions were used as the multielectron basis for the two-step spin–orbit coupling calculation (Llusar et al. 1996; Zeng et al. 2011) through the Breit–Pauli Hamiltonian (Berning et al. 2000) as implemented in MOLPRO.

2018AandA...619A..13V__Saviane_et_al._2012_Instance_3

The EWs were measured with the methods described in Vásquez et al. (2015). As in Paper I, we used the sum of the EWs of the two strongest CaT lines (λ8542, λ8662) as a metallicity estimator, following the Ca II triplet method of Armandroff & Da Costa (1991). Different functions have been tested in the literature to measure the EWs of the CaT lines, depending on the metallicity regime. For metal-poor stars ([Fe/H] ≲ −0.7 dex) a Gaussian function was used with excellent results (Armandroff & Da Costa 1991), while a more general function (such as a Moffat function or the sum of a Gaussian and Lorentzian, G + L) is needed to fit the strong wings of the CaT lines observed in metal-rich stars (Rutledge et al. 1997; Cole et al. 2004). Following our previous work (Gullieuszik et al. 2009; Saviane et al. 2012) we have adopted here a G+L profile fit. To measure the EWs, each spectrum was normalised with a low-order polynomial using the IRAF continuum task, and set to the rest frame by correcting for the observed radial velocity. The two strongest CaT lines were fitted by a G+L profile using a non-linear least squares fitting routine part of the R programming language. Five clusters from the sample of Saviane et al. (2012) covering a wide metallicity range were re-reduced and analysed with our code to ensure that our EWs measurements are on the same scale as the template clusters used to define the metallicity calibration. Figure 5 shows the comparison between our EWs measurements and the line strengths measured by Saviane et al. (2012) (in both cases the sum of the two strongest lines) for the five calibration clusters. The observed scatter is consistent with the internal errors of the EW measurements, computed as in Vásquez et al. (2015). The measurements show a small deviation from the unity relation, which is more evident at low metallicity. A linear fit to this trend gives the relation: ΣEW(S12) = 0.97 ΣEW(this work) + 0.21, with an rms about the fit of 0.13 Å. This fit is shown in Fig. 5 as a dashed black line. For internal consistency, all EWs in this work have been adjusted to the measurement scale of Saviane et al. (2012) by using this relation. In Table 3 we provide the coordinates, radial velocities, and the sum of the equivalent widths for the cluster member stars, both measured (“m”) and corrected (“c”) to the system of Saviane et al. 2012.

2020MNRAS.496.3448D__Joy,_Sahni_&_Starobinsky_2008_Instance_1

The aforementioned form of power-law primordial power spectrum is a prediction of inflation where the scalar field (inflaton) slowly rolls down to the bottom of the flat inflationary potential. With the constraints on the tilt and an upper bound on the amplitude of tensor perturbation with respect to scalar perturbation, various surveys have ruled out a wide class of models. However, fundamental questions such as the energy scale of inflation and the detailed shape of the potential remain unanswered. Note that any changes in the nearly flat potential will eventually lead to certain features in the spectrum. Local glitches in the potential including rapid change of its amplitude, or the break in its first or second derivatives (Starobinsky 1992; Starobinsky 1998; Adams, Cresswell & Easther 2001; Covi et al. 2006; Joy, Sahni & Starobinsky 2008; Joy et al. 2009; Hazra et al. 2010; Miranda, Hu & Adshead 2012; Benetti 2013; Cadavid & Romano 2015; Chluba, Hamann & Patil 2015), false vacuum decay (leading to open inflation, in particular) (Linde 1999; Linde, Sasaki & Tanaka 1999; Bousso, Harlow & Senatore 2014), or an inflection point in the potential (Allahverdi & Mazumdar 2006; Jain et al. 2009), or oscillations in the potential (Ashoorioon & Krause 2006; Pahud, Kamionkowski & Liddle 2009; Biswas, Mazumdar & Shafieloo 2010; Flauger et al. 2010; Aich et al. 2013; Hazra 2013; Peiris, Easther & Flauger 2013; Easther & Flauger 2014; Meerburg, Spergel & Wandelt 2014; Motohashi & Hu 2015; Miranda et al. 2016) all lead to local and non-local oscillations in the spectrum. Direct reconstruction of the primordial spectrum from the Planck data (Hazra et al. 2014c) hints at large-scale oscillations, an intermediate-scale burst of oscillations, and persistent high-frequency oscillations within intermediate to small scales. While these types of features can be obtained by different potentials, in this work we will be using the WWI, which is known the provide these local and non-local features in a unified framework.

2021ApJ...908...95H__Goto_et_al._2011_Instance_1

Star-forming galaxies at redshifts z ∼ 1–3 probe the cosmic epoch when most of the stellar mass assembly in the universe took place (Madau & Dickinson 2014, and references therein). A better understanding of star formation (SF) during this epoch is therefore imperative to understand SF across cosmic time. Locally, less than 5% of the galaxy population has a star formation rate (SFR) that is significantly higher than the empirical main sequence for star-forming galaxies, i.e., the tight correlation (∼0.3 dex) between the SFR and stellar mass, M⋆ (Brinchmann et al. 2004; Elbaz et al. 2007, 2011; Noeske et al. 2007; Goto et al. 2011; Rodighiero et al. 2011; Sargent et al. 2012; Whitaker et al. 2012, 2014; Salmon et al. 2015). These often-called starburst galaxies, with an IR luminosity LIR ∼ (0.1–5) × 1012 L⊙ (e.g., Sanders & Mirabel 1996; Downes & Solomon 1998), become increasingly more common at high z. In fact, (sub)millimeter number counts reveal that galaxies with LIR > 1012–13 L⊙, at z > 0.5, are many hundreds of times more likely to exist than in the local universe (Blain et al. 2002; Chapman et al. 2005; Berta et al. 2011; Magnelli et al. 2011; Béthermin et al. 2012; Magnelli et al. 2013; Casey et al. 2013, 2014; Geach et al. 2013; Simpson et al. 2014; Strandet et al. 2016; Brisbin et al. 2017). Meanwhile, the cosmic molecular gas density also peaks at z ∼ 1–3 (Decarli et al. 2014, 2016a, 2016b, 2019; Walter et al. 2014; Lentati et al. 2015; Pavesi et al. 2018; Liu et al. 2019; Riechers et al. 2019). This suggests a strong link between molecular gas and SF. Rest-frame far-IR (FIR) measurements of spectral lines and thermal dust continuum emission have been used to investigate the cooling and heating processes of the interstellar medium (ISM) in star-forming galaxies; however, the physical conditions at high z are still, in general, poorly investigated (Popesso et al. 2012; Bothwell et al. 2013; Carilli & Walter 2013; Genzel et al. 2013; Yang et al. 2017; Tacconi et al. 2018, 2020; Aravena et al. 2020; Birkin et al. 2020; Boogaard et al. 2020; Lenkić et al. 2020).

2015MNRAS.450...53H__Muñoz_et_al._2014_Instance_2

Using moving meshes helps reduce the angular momentum errors from advection in grid codes. We have run >200 iterations of this test problem using the public version of fvmhd3d, systematically varying choices like the mesh regularization scheme, mesh ‘drifting’ (whether to use a strictly Lagrangian drift, or locally smoothed velocity, or regularized drift), initial mesh geometry, and boundary conditions. In both fvmhd3d and more limited tests with arepo, we find that running in the ‘simplest’ initial configuration (an initial Cartesian mesh with outflow boundary conditions, with the default mesh regularization scheme used for all other test problems shown here), the disc goes unstable and the angular momentum evolution tends to be corrupted within a few orbits (similar to the fixed-grid cases). Unfortunately, some significant errors in angular momentum evolution are difficult to avoid in moving-mesh codes, as has been discussed extensively in e.g. Duffell & MacFadyen (2012), Ivanova et al. (2013), Mocz et al. (2014) and Muñoz et al. (2014). In a shearing disc, if the cells adapt in a truly Lagrangian manner, then they are inevitably deformed into a highly sheared/irregular shape (Muñoz et al. 2014). This leads to other errors. As soon as they become non-spherical (or more accurately fail to be radially symmetric about their own cell centre of mass), then mass advection between cells necessarily leads to additional angular momentum errors (indeed, the angular momentum of an irregular cell cannot be defined exactly but only to the same order of integration accuracy as the local velocity gradient estimator). This is akin to the errors in our MFV method. More importantly, if some regularization procedure is used to keep the cell shapes ‘regular’ (as is necessary in any moving-mesh code used for this problem), then the regularization means the cells cannot move entirely with the fluid and the gas must be advected over the cells. This re-introduces some of the same (more serious) errors we saw with stationary-grid methods (specifically, see Ivanova et al. 2013, equation 53). This means that the results for moving meshes are quite sensitive to choices like the mesh ‘stiffness’, regularization procedure, and in particular the choice of boundary conditions for the mesh-generating points (since the rigid Voronoi volume partition can lead to a ‘mesh tension’ effect, whereby regularization-induced distortions in the central regions propagate outwards ‘through’ the mesh; Springel 2010). So there are ways to improve the situation on this problem – for this reason, we do not show a single ‘standard’ moving-mesh result, because significantly different results are obtained if we make just small changes to the mesh-regularization procedure in each code. However, like with AMR codes, the most effective methods for eliminating angular momentum errors in moving meshes generally depend on knowing the problem geometry ahead of time. For example, Duffell & MacFadyen (2012) design a moving grid which is a series of cylindrical shells free to rotate independently about a shared axis (the disco code); Muñoz et al. (2013) use a carefully chosen initial grid configuration with a specially designed boundary condition designed to prevent inward propagation of ‘mesh deformation’; these help considerably, but must be fine-tuned to the exact disc configuration.

2015MNRAS.450..630S__Moore_et_al._1996_Instance_1

While there are increasing efforts to try to explain the SFR dependence on the environment, by conducting surveys at high redshift (e.g. Hayashi et al. 2010; Matsuda et al. 2011; Sobral et al. 2011; Muzzin et al. 2012; Koyama et al. 2013; Darvish et al. 2014; Tal et al. 2014), so far such studies have not been able to fully reveal the physical processes leading to the ultimate quenching of (satellite) star-forming galaxies (e.g. Peng et al. 2010; Muzzin et al. 2012, 2014). Several strong processes have been proposed and observed, such as harassment (e.g. Moore et al. 1996), strangulation (e.g. Larson, Tinsley & Caldwell 1980) and ram pressure stripping (e.g. Gunn & Gott 1972; Fumagalli et al. 2014). Observations are also showing a variety of blueshifted rest-frame ultraviolet (UV) absorption lines which indicate that most star-forming galaxies at least at z ∼ 1–2 are able to drive powerful gas outflows (e.g. Shapley et al. 2003; Weiner et al. 2009; Kornei et al. 2012) which may play a significant role in quenching, particularly if those happen in high-density environments. Evidence of such galactic winds have also been seen in e.g. Förster Schreiber et al. (2009) through broad components in the rest-frame optical Hα and [N ii] emission line profiles (e.g. Genzel et al. 2011). Spatially resolved observations allow for constraints on the origin of the winds within galaxies, and on the spatial extent of the outflowing gas, which are essential to derive mass outflow rates. In field environments, it is expected that such outflows will not be able to escape the halo (as long as it is massive enough and it is not a satellite), and in many conditions would likely come back and further fuel star formation (e.g. Hopkins et al. 2014). However, in the most massive clusters, such strong outflows will likely result in significant amounts of gas being driven out of the subhaloes that host star-forming galaxies, enriching the intracluster medium (ICM) and quickly quenching star-forming galaxies with the highest SFRs/highest outflow rates.

2018AandA...618A..67C__Moriguchi_et_al._(2002)_Instance_2

The close proximity in the sky of M 16 and M 17, two of the nearest giant HII regions of our galactic neighborhood lying at a similar distance from the Sun, naturally leads to the question of whether they are physically related, and whether they may share a common origin (Moriguchi et al. 2002; Oliveira 2008; Nishimura et al. 2017). Both giant HII regions are projected on the contour of a giant bubble-shaped structure, outlined in the distribution of HI and CO emission as first noted by Moriguchi et al. (2002). Evidence for triggered star formation in M 16 has been examined in detail by Guarcello et al. (2010), who concluded it had been induced externally, and not by the activity of its associated cluster NGC 6611. This suggests that the formation of M 16 and M 17 could have been triggered by the expansion of the bubble, powered by a previous generation of massive stars near its center, thus representing an example of triggered star formation on the scale of several tens of parsecs (Elmegreen 1998). Given the ages of the giant HII regions, the timescale of expansion of a wind-blown bubble, and the short lifetimes of massive stars, it is to be expected that the most massive members of that previous generation may have exploded as supernovae several Myr ago. The spatial dispersion of the members of the association that must have taken place progressively during its existence, combined with the distance of 2 kpc to the M 16–M 17 complex and the large number of unrelated foreground and background stars in that general direction, would make it extremely difficult to identify even its currently hottest members still remaining on the main sequence. Moriguchi et al. (2002) noted the presence of O and early B stars in the area and proposed that they were part of a massive star population responsible for having caused the bubble, but a review of their properties shows them to be generally too bright to be at the distance of the bubble and the giant HII regions, and instead are more likely members of a foreground population.

2017AandA...606A..17M__Kennicutt_(1998)_Instance_2

The SFR reported in Table C.1 refers to a stellar mass range from Mlow = 0.1M⊙ to Mup = 100M⊙, is averaged over the past Δt = 100 Myr, and was calculated using the standard SFR(LIR) relationship from Kennicutt (1998; here scaled to a Chabrier 2003, IMF) (1)\begin{equation} \label{eq:sfr} \textit{SFR}=10^{-10}\times L_{\rm IR}[{L}_{\sun}]\, {M}_{\sun}~{\rm yr}^{-1}. \end{equation}SFR=10-10×LIR[L⊙] M⊙yr-1.This calibration relies on the starburst synthesis models of Leitherer & Heckman (1995), and it is based on the assumption of solar metallicity, and an optically thick (τdust ≫ 1) starburst region, in which case LIR is a good proxy of the system’s bolometric luminosity (LIR ≃ Lbol), and hence a sound, calorimetric probe of the obscured, current stellar birth rate. A possible caveat is that the contribution to the dust heating by more evolved stellar populations (the cirrus component; e.g. Helou 1986; Lonsdale Persson & Helou 1987; Walterbos & Greenawalt 1996) is not taken into account. If the cirrus ISM component heated by the more general galactic UV radiation field contributes to LIR, then the Kennicutt (1998) relationship overestimates the SFR. Another issue is the fact that some percentage of the UV photons can escape the starburst region without being absorbed, and hence are not reprocessed into IR photons (indeed, some of our SMGs are visible in the rest-frame UV images; Miettinen et al. 2017b). The MAGPHYS code also gives the SFR as an output, and contrary to the aforementioned LIR diagnostic, the model permits for the heating of the dust by older and longer-lasting stellar populations. We found that the SFR(LIR) is somewhat higher on average than SFRMAGPHYS: the SFR(LIR) /SFRMAGPHYS ratio was found to range from 0.47 to 6.92 with a median of \hbox{$1.31^{+0.83}_{-0.17}$}1.31-0.17+0.83, where the ± errors represent the 16th–84th percentile range (see the corresponding panel in Fig. 2). If, instead of Δt = 100 Myr, the aforementioned comparison is done by using the SFRMAGPHYS values averaged over the past Δt = 10 Myr, the median SFR(LIR) /SFRMAGPHYS ratio is found to be \hbox{$1.15^{+0.38}_{-0.27}$}1.15-0.27+0.38, which is consistent with the results obtained by da Cunha et al. (2015). Unless otherwise stated, in our subsequent analysis we use the SFR averaged over the past 100 Myr as calculated using Eq. (1).

2018MNRAS.480.4931V__Dokkum_2001_Instance_1

We made use of the SALT product data generated by the in-house pipeline called PySALT (Crawford et al. 2010), which mosaics the individual CCD data to a single FITS file, corrects for cross-talk effects, and performs bias and gain corrections. We then carried out further reduction steps using our own custom tools, written in Python/PyRAF, which consist of several modules designed to handle specific steps of processing. First, we trim the regions of the CCD not containing any usable data and then fill the CCD gaps using a gradient fill. To do this, we calculate the gradient across the gap by measuring the flux values of the pixels to the right and the left of the gap, computing the difference and normalizing it to the number of pixels belonging to the CCD gap (the gradient). We then use the gradient to determine the flux values for pixels in each row of the image and replace the zero-value pixels by these flux values. This step is important to prevent discontinuities in the brightness distribution which affect the flat-fielding process which relies on the modelling of the large-scale illumination structure. Another advantage of this gradient fill is seen in the subsequent step of background subtraction. A CCD gap which appears straight in the original images becomes curved as a result of the coordinate transformation step. This causes background subtraction to result in discontinuous patches. By filling the CCD gap, these patches can be avoided. We then remove the cosmic rays using the LACosmic algorithm (van Dokkum 2001). Calibration in wavelength is achieved using arc lamps and has typical error of ±0.35 Å. We use the arc lamp to determine a coordinate transformation to ‘straighten’ frames so that every column corresponds to a unique wavelength making the background sky shape fitting and subtraction possible. Using the spectrophotometric standard data, we determine the relative flux calibration. We then check individual frames for alignment and co-add them. During this step, a standard deviation frame is constructed from which an error frame is obtained. Before the co-addition, to have consistent noise characteristics, we equalize the effective exposures of the frames – this is needed because the pupil of SALT changes by design during the observation. If Oi(x, y) are the individual observed frames, the final spectrum F(x, y) and error spectrum E(x, y) can be written as, (1) \begin{equation*} F(x,y) = \left\langle O(x,y) \right\rangle = \frac{\sum _i^N O_i(x, y)}{N}, \end{equation*} (2) \begin{equation*} E(x,y) = \sqrt{\frac{\sum _i^N (O_i(x,y) - \left\langle O(x,y)\right\rangle)^2}{N-1}}. \end{equation*} An alternate way of obtaining the error frames is to start from the raw CCD images whose noise properties are relatively well understood to try to model the effects of every single reduction step on the errors. This is, however, very complicated and so we adopt the above technique of determining our final error frame. Since we are combining only six to eight frames, we might overestimate our errors, but this overestimation does not affect the findings presented in the paper. Our final step is to correct for foreground extinction and this is done using the deredden task in IRAF.

2021MNRAS.500.2336Y__Lin_et_al._2020_Instance_1

Various surveys of SNRs in our Galaxy and nearby galaxies have been carried out at radio, X-ray, Infrared (IR), and optical wavelengths. The first extragalactic SNR candidates were identified in the LMC by Mathewson & Healey (1964) and later confirmed with a combination of radio and optical techniques by Westerlund & Mathewson (1966). To date, a total of 60 SNRs have been confirmed in the LMC with an additional 14 suggested candidates (Maggi et al. 2016; Bozzetto et al. 2017; Maitra et al. 2019). However, sensitivity and resolution limitations severely reduce the effectiveness of the past and present generations of radio and X-ray searches for SNRs in galaxies beyond the Small and Large Magellanic Clouds (MCs) (Goss et al. 1980; Long et al. 1981; Cowan & Branch 1985; Matonick et al. 1997; Matonick & Fesen 1997; Millar, White & Filipovic 2012; Galvin & Filipovic 2014; Sasaki et al. 2018; Lin et al. 2020; Sasaki 2020). As a result, optical studies have produced the largest number (∼1200) of new extra-Magellanic SNR candidates. Optical extragalactic searches for SNRs are mainly done by using an emission line ratio criterion of the form [S ii]/H α > 0.4–0.5 (Mathewson & Clarke 1973; Dodorico, Dopita & Benvenuti 1980; Fesen 1984; Blair & Long 1997; Matonick & Fesen 1997; Dopita et al. 2010b; Lee & Lee 2014; Vučetić et al. 2019b, a, 2018; Lin et al. 2020). This criterion separates shock-ionization from photoionization in SNRs from H ii regions and Planetary Nebulae (PNe) (Frew & Parker 2010). SNR radiative shocks collisionally excite sulphur ions in the extended recombination region resulting in S+, hence the larger contribution of [S ii] accounting for an increase of the [S ii] to H α ratio. In typical H ii regions, sulphur exists predominantly in the form of S++, yielding low [S ii] to H α emission ratios. Ratios from narrow-band imaging are usually verified spectroscopically, since [N ii] lines at 6548 and 6584 Å can contaminate the H α images at an unknown and variable level. Spectroscopic observations of such emission nebulae also can provide other evidence of shock heating, such as strong [O i] λ6300 emission, elevated [N ii] to H α with respect to H ii regions, or high [O iii] electron temperatures, verifying the candidate as being an SNR (Blair, Kirshner & Chevalier 1981, 1982; Long et al. 1990; Smith et al. 1993; Blair & Long 1997). Although somewhat biased as an isolated criterion, this method is proven and a good way of identifying ordinary radiatively cooling SNRs in nearby galaxies. We note that young, Balmer-dominated SNRs (Chevalier, Kirshner & Raymond 1980) would be missed by this criterion.

2018ApJ...856..136P__Burkhart_et_al._2010_Instance_2

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).

2018AandA...617A..86L__Tian_2017_Instance_2

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).

2016MNRAS.461.1719C__Harris_et_al._2012_Instance_1

HATLAS12-00 had already been identified as a candidate gravitationally lensed galaxy as a result of its high submm flux (i.e. F500 > 100 mJy), red Herschel colours and the lack of a bright optical or radio counterpart (see e.g. Negrello et al. 2010 for a discussion of the selection of lens candidates in H-ATLAS and other Herschel surveys). This source was therefore observed spectroscopically in the submm. A CO spectroscopic redshift of 3.26 was first suggested by Z-spec (Bradford et al. 2004) observations, then subsequently confirmed by observations by the CARMA interferometer (Leeuw et al., in preparation) and the Zpectrometer instrument (Harris et al. 2007) on the Greenbank Telescope (Harris et al. 2012; see also Fu et al. 2012). Additional followup observations in the optical, near-IR, submm and other wavelengths were targeted at the lensed z = 3.26 source and the foreground objects responsible for the lensing, resulting in detailed analyses of this lensing system by Fu et al. (2012) and Bussmann et al. (2013). Their conclusions are that the z = 3.26 source HATLAS12-00 is subject to gravitational lensing, with a magnification of 9.6 ± 0.5 in both the submm continuum and CO, and 16.7 ± 0.8 in the K′ band, by two foreground galaxies, one at a spectroscopically determined redshift of 1.22, and another with photometry suggesting that it lies at a similar redshift. The submm photometry of HATLAS12-00 at 890 μm acquired with the Submillimeter Array (SMA) as part of this programme (Fu et al. 2012; Bussmann et al. 2013) is fully consistent with the 870 μm and 850 μm fluxes derived for this source from the LABOCA and SCUBA2 data to be presented here. The spectral energy distribution (SED) of the lensed source, after correcting for the lensing amplification, is well matched by the optically thick SED model for Arp220 from Rangwala et al. (2011), with a lensing-corrected far-IR luminosity of 1.2 ± 0.2 × 1013 L⊙, and an implied star formation rate of 1400 ± 300  M⊙ yr−1. In many ways the unlensed properties of this object match those of the broader population of bright submm selected galaxies first discovered by the SCUBA submm imager (see e.g. Chapman et al. 2005; Clements et al. 2008; Michałowski, Hjorth & Watson 2010). The unlensed 870 μm flux of this object would be ∼7.7 mJy.

2022MNRAS.513.3458B__Robertson_et_al._2019_Instance_1

Among the most viable mechanisms of cusp-core transformation that require changes to the assumed cosmogony is one that was proposed specifically as a possible solution to the cusp-core problem. It proposes that the DM is in fact not collisionless but self-interacting (SIDM; Spergel & Steinhardt 2000; Yoshida et al. 2000; Davé et al. 2001; Colín et al. 2002; Vogelsberger, Zavala & Loeb 2012; Rocha et al. 2013; see Tulin & Yu 2018 for a review). In SIDM, particles can exchange energy and momentum through elastic scattering, causing an outside-in energy redistribution within the centre of DM haloes, resulting in the formation of an isothermal core. The time-scale on which an initially cuspy SIDM halo forms a flat and isothermal core is roughly given by the time it takes for each DM particle in the inner halo to scatter at least once (Vogelsberger et al. 2012; Rocha et al. 2013). The strength of the self-interaction in SIDM models is parametrized in terms of the momentum transfer cross-section per unit mass, σT/mχ. Depending on the specific SIDM model, σT/mχ can either be constant or dependent on the relative velocity between the two scattering DM particles. SIDM is an efficient mechanism of cusp-core transformation in dwarf-size haloes for $\sigma _T/m_\chi \gtrsim 1\, {\rm cm^2g^{-1}}$, whereas SIDM haloes are virtually indistinguishable from CDM haloes if $\sigma _T/m_\chi \lesssim 0.1\, {\rm cm^2g^{-1}}$ (Zavala, Vogelsberger & Walker 2013). The most stringent and precise constraints on the self-interaction cross-section have been put on the scales of galaxy clusters (e.g. Robertson, Massey & Eke 2017; Robertson et al. 2019) and large elliptical galaxies (Peter et al. 2013), where observations require that $\sigma _T/m_chi \lesssim 1\, {\rm cm^2g^{-1}}$. On smaller scales, Read, Walker & Steger (2018) concluded that $\sigma _T/m_\chi \lesssim 0.6\, {\rm cm^2g^{-1}}$, based on their findings that the central density profile of the MW dwarf spheroidal galaxy Draco is cuspy (see also the SIDM results of Valli & Yu 2018). Moreover, based on a DM only analysis of the updated too-big-to-fail problem, Zavala et al. (2019) concluded that SIDM models with a constant cross-section of $\sigma _T/m_\chi \sim 1\, {\rm cm^2g^{-1}}$ fail to explain the apparently large central densities of the host haloes of the ultra-faint satellites of the MW (Errani, Peñarrubia & Walker 2018). It should be pointed out that the constraints on σT/mχ on the scale of dwarf galaxies are affected by significantly larger systematic uncertainties than on the scales of galaxy clusters or elliptical galaxies. Moreover, Zavala et al. (2019) demonstrate that SIDM with a strongly velocity-dependent self-interaction cross-section may provide a natural explanation for the observed diversity in the rotation curves of the MW dwarf spheroidals (see also Correa 2021). The strong dependence of the self-interaction cross-section on the typical DM velocities would create a bimodal distribution of rotation curves in the MW satellites in which the heavier haloes have constant density cores while the lighter haloes have undergone gravothermal collapse and have very steep central cusps as a consequence. The same mechanism of gravothermal collapse might be accelerated by tidal interactions in the environment of the MW leading to an agreement between constant cross-section SIDM models with $\sigma _T/m_\chi \sim 3\, {\rm cm^2g^{-1}}$ and the internal kinematics of MW satellites (e.g. Kahlhoefer et al. 2019; Sameie et al. 2020).

2022AandA...666A.107S__BeyondPlanck_2022_Instance_1

While new data will refine our understanding of the interplanetary medium, it is necessary for future models to be consistent with both new and archival data. It is precisely this need that motivates the COSMOGLOBE project1, which aims to create a framework that will allow for the refinement of astrophysical models jointly with the raw data from complementary experiments. This form of joint analysis is already being explored within the framework of WMAP (Watts et al. 2022) and LiteBIRD (Aurlien et al., in prep.), in combination with Planck Low Frequency Instrument data (BeyondPlanck 2022). ZE is an especially promising direction for joint analysis, in part because HFI made observations of ZE at complementary wavelengths to DIRBE. While the DIRBE model was modified during the HFI analysis, no attempt was made to improve upon the geometrical representation of the model components using the larger effective dataset. Our understanding of the interplanetary medium has improved since the development of the DIRBE IPD model (see, e.g., Reach et al. 1997; Reach 2010a,b). The AKARI satellite, which observed in the infrared at wavelengths between 6 and 180 µm, detected small-scale structures in the ZE which were not well-characterized by the DIRBE model (Ootsubo et al. 2016). While the DIRBE model has been successful in describing the large-scale diffuse ZE, modern high-resolution high-frequency and infrared experiments will require IPD models that can more effectively resolve the small-scale structures in the ZE. As such, an update of the community state-of-art ZE model is long overdue. In this COSMOGLOBE framework, such a model refinement is a natural byproduct of a joint analysis of DIRBE, HFI, and other data. To improve upon this model, it is essential to have the model agree with the data at all wavelengths. The modeling of ZE is in the process of being implemented in the Commander framework, which will ultimately be used for the joint processing of HFI and DIRBE data. However, a stand-alone code is useful for agile model building and data analysis. As such, ZodiPy will function as the first step toward this goal in the COSMOGLOBE framework, in addition to making ZE corrections on arbitrary data more accessible to the community.

2017AandA...599A..97H__Carretta_et_al._2009b_Instance_1

Amongst the oldest stellar systems known to exist in the Milky Way (MW) are metal-poor globular clusters (GCs). These accumulations of stars do not seem to have undergone substantial star formation for extended periods. Given the limited quality of the available data, for a long time color-magnitude diagrams (CMDs) of GCs appeared to be narrow and could be readily described by a single isochrone. These observations have justified the establishment of the long-lasting paradigm that considers CGs as prime examples of simple stellar populations (SSPs), that is, the results of very short bursts of star formation in their natal clouds. However, improved photometric precision indicates the presence of sub-populations in the cluster CMDs that are inconsistent with the SSP assumption, for a number of luminous GCs in a variety of bandpasses. Thus, early detections of chemical abundance variations (e.g., Cohen 1978) could be more easily explained in a scenario involving several populations. Moreover, in recent years evidence has grown supporting the statement that GCs are generally composed of two or three chemically distinct populations. These subpopulations are separated by a few tens to hundreds of Myr in age and show vastly varying abundances of light elements such as C, N, O, Na, Mg, and Al (see, e.g., Carretta et al. 2009b; Gratton et al. 2012, and references therein). Theoretical considerations (see, e.g., D’Ercole et al. 2008, 2011) imply that GCs could have lost the majority of the initial stellar content of the first population, which consequently should have ended up in the Galactic halo. In fact, numerous studies found metal-poor GCs to be consistent with the abundance trends of the MW halo at equally low metal content (e.g., Pritzl et al. 2005; Koch et al. 2009; Koch & McWilliam 2014; Villanova et al. 2016). We address this scenario by adding NGC 6426 to the short list of metal-poor clusters with available information on detailed chemical abundances. There are only two GCs in the Harris catalog (Harris 1996, 2010 edition) more metal poor than NGC 6426. At 12.9 ± 1.0 Gyr, the cluster is the oldest in the age compilation by Salaris & Weiss (2002). At a galactocentric distance of Rgc = 14.4 kpc and a galactic latitude of 16.23° it is located in the transition region between inner and outer halo. Previous studies found consistent [Fe/H]1 values: −2.20 ± 0.17 dex (Zinn & West 1984), −2.33 ± 0.15 (Hatzidimitriou et al. 1999), and −2.39 ± 0.04 dex (Dias et al. 2015). The latter value originates from the very first spectroscopic analysis of NGC 6426 at low resolution, which also stated [Mg/Fe] = 0.38 ± 0.06 dex. To date, there has been no study further addressing the detailed metal content of this cluster.

2021MNRAS.500.2336Y___2018_Instance_1

Various surveys of SNRs in our Galaxy and nearby galaxies have been carried out at radio, X-ray, Infrared (IR), and optical wavelengths. The first extragalactic SNR candidates were identified in the LMC by Mathewson & Healey (1964) and later confirmed with a combination of radio and optical techniques by Westerlund & Mathewson (1966). To date, a total of 60 SNRs have been confirmed in the LMC with an additional 14 suggested candidates (Maggi et al. 2016; Bozzetto et al. 2017; Maitra et al. 2019). However, sensitivity and resolution limitations severely reduce the effectiveness of the past and present generations of radio and X-ray searches for SNRs in galaxies beyond the Small and Large Magellanic Clouds (MCs) (Goss et al. 1980; Long et al. 1981; Cowan & Branch 1985; Matonick et al. 1997; Matonick & Fesen 1997; Millar, White & Filipovic 2012; Galvin & Filipovic 2014; Sasaki et al. 2018; Lin et al. 2020; Sasaki 2020). As a result, optical studies have produced the largest number (∼1200) of new extra-Magellanic SNR candidates. Optical extragalactic searches for SNRs are mainly done by using an emission line ratio criterion of the form [S ii]/H α > 0.4–0.5 (Mathewson & Clarke 1973; Dodorico, Dopita & Benvenuti 1980; Fesen 1984; Blair & Long 1997; Matonick & Fesen 1997; Dopita et al. 2010b; Lee & Lee 2014; Vučetić et al. 2019b, a, 2018; Lin et al. 2020). This criterion separates shock-ionization from photoionization in SNRs from H ii regions and Planetary Nebulae (PNe) (Frew & Parker 2010). SNR radiative shocks collisionally excite sulphur ions in the extended recombination region resulting in S+, hence the larger contribution of [S ii] accounting for an increase of the [S ii] to H α ratio. In typical H ii regions, sulphur exists predominantly in the form of S++, yielding low [S ii] to H α emission ratios. Ratios from narrow-band imaging are usually verified spectroscopically, since [N ii] lines at 6548 and 6584 Å can contaminate the H α images at an unknown and variable level. Spectroscopic observations of such emission nebulae also can provide other evidence of shock heating, such as strong [O i] λ6300 emission, elevated [N ii] to H α with respect to H ii regions, or high [O iii] electron temperatures, verifying the candidate as being an SNR (Blair, Kirshner & Chevalier 1981, 1982; Long et al. 1990; Smith et al. 1993; Blair & Long 1997). Although somewhat biased as an isolated criterion, this method is proven and a good way of identifying ordinary radiatively cooling SNRs in nearby galaxies. We note that young, Balmer-dominated SNRs (Chevalier, Kirshner & Raymond 1980) would be missed by this criterion.

2020ApJ...891...10L__Song_et_al._2009_Instance_1

We need to build data sets to train and evaluate the model. Then the data sets are prepared in the following way: (1) if the AR does not flare within 24 hr after the observation time, the No-flare (weaker than C1.0) label is assigned to the magnetogram sample in the same AR. (2) If the C/M/X-level flare occurs within 24 hr after the observation time, the corresponding flare label (i.e., C, M, or X) is assigned to the magnetogram sample. Note that there are a number of ARs producing recurring flares with different flare levels within 24 hr. For the first flare of one AR, the corresponding flare label is assigned to the magnetogram sample within 24 hr after the observation time. Then for the following flares of the same AR, the corresponding labels are assigned to the magnetogram sample in the period from the end of its prior flare to the end of this flare. (3) We adopt a four-level AR classification scheme based on the maximum GOES-level flare an AR ever yields (Song et al. 2009; Yuan et al. 2010; Liu et al. 2017). In other words, ARs are further categorized into four levels (i.e., No-flare, C, M, and X) if they yield at least one flare with such GOES-level criterion but no flares with higher GOES-level criterion: “Level = X” indicates that an AR yields at least one X-level flare; “Level = M” indicates that an AR yields at least one M-level flare but no X-level flares; “Level = C” indicates that an AR yields at least one C-level flare but no M/X-level flares; “Level = No-flare” indicates that an AR only yields microflares (weaker than C1.0 flares). Finally, we gather 870 ARs and 136134 magnetogram samples in total, including 443 X-level, 6534 M-level, 72412 C-level, and 56745 No-flare level samples. Note that the magnetogram samples with multiple ARs (Bobra et al. 2014) are not included in our work. For the ≥M class, magnetogram samples of M/X-level flare in an AR are defined as positive class, while all the others are defined as negative class. For the ≥C class, magnetogram samples of C/M/X-level flare in an AR are defined as positive class, while all the others are defined as negative class.

2021MNRAS.506.1962S__Nordlander_&_Lind_2017_Instance_1

Among the odd Z elements, we derived the abundances of Na, Al, K, and Sc using the high-resolution spectra and the details of the lines are given in Table A1. The Na D lines were not used as the lines are too strong for deriving the abundances. So, we depended on the weaker lines 5682 Å and 5688 Å to derive the Na abundance. The NLTE corrections for these Na i lines were performed from Lind et al. (2011) whenever available and the average values are listed in Table 3. Al i lines at 6696 and 6698 are used for deriving the Al abundance and these lines have negligible contributions from NLTE effects (Baumueller & Gehren 1997; Nordlander & Lind 2017). For the case of K, whenever the feature at 7698.98 Å has contributions from telluric features, the abundance from 7664.87 Å is quoted, otherwise the average of the abundances from both the lines is quoted in Table 3. Both these lines are sensitive to NLTE effects (Takeda et al. 2002; Kobayashi et al. 2006; Andrievsky et al. 2010; Prantzos et al. 2018; Reggiani et al. 2019) and the NLTE corrections depend on the effective temperature and surface gravity of the model. Using the NLTE grid provided in Reggiani et al. (2019), we have estimated the NLTE corrections for the program stars and the NLTE corrected values are given in Table 3. The NLTE grid does not cover very low log g values, so we could not estimate the NLTE corrections for HE 1152−0355 and HE 0314−0143. We could not measure the K abundance in HD 5223 due to the contamination from telluric features. For the case of Sc abundance, we could not find any study of Sc line formation in NLTE except in Zhang, Gehren & Zhao (2008) where they have studied the NLTE effects only for the sun and identified negligible NLTE effects for Sc ii lines. So, we report only LTE results for this element in Table 3. The resolution of the NIR spectra was too low to resolve the HF feature from the nearby features, so we could not derive F abundance in these stars.

2015AandA...584A..75V__Essen_et_al._(2014)_Instance_4

The data presented here comprise quasi-simultaneous observations during secondary eclipse of WASP-33 b around the V and Y bands. The predicted planet-star flux ratio in the V-band is 0.2 ppt, four times lower than the accuracy of our measurements. Therefore, we can neglect the planet imprint and use this band to measure the stellar pulsations, and most specifically to tune their current phases (see phase shifts in von Essen et al. 2014). Particularly, our model for the light contribution of the stellar pulsations consists of eight sinusoidal pulsation frequencies with corresponding amplitudes and phases. Hence, to reduce the number of 24 free parameters and the values they can take, we use prior knowledge about the pulsation spectrum of the star that was acquired during von Essen et al. (2014). As the frequency resolution is 1/ΔT (Kurtz 1983), 3.5 h of data are not sufficient to determine the pulsations frequencies. Therefore, during our fitting procedure we use the frequencies determined in von Essen et al. (2014) as starting values plus their derived errors as Gaussian priors. As pointed out in von Essen et al. (2014), we found clear evidences of pulsation phase variability with a maximum change of 2 × 10-3 c/d. In other words, as an example after one year time a phase-constant model would appear to have the correct shape with respect to the pulsation pattern of the star, but shifted several minutes in time. To account for this, the eight phases were considered as fitting parameters. The von Essen et al. (2014) photometric follow-up started in August, 2010, and ended in October, 2012, coinciding with these LBT data. We then used the phases determined in von Essen et al. (2014) during our last observing season as starting values, and we restricted them to the limiting cases derived in Sect. 3.5 of von Essen et al. (2014), rather than allowing them to take arbitrary values. The pulsation amplitudes in δ Scuti stars are expected to be wavelength-dependent (see e.g. Daszyńska-Daszkiewicz 2008). Our follow-up campaign and these data were acquired in the blue wavelength range. Therefore the amplitudes estimated in von Essen et al. (2014), listed in Table 1, are used in this work as fixed values. This approach would be incorrect if the pulsation amplitudes would be significantly variable (see e.g., Breger et al. 2005). Nonetheless, the short time span of LBT data, and the achieved photometric precision compared to the intrinsically low values of WASP-33’s amplitudes, make the detection of any amplitude variability impossible.

2016MNRAS.460.3472E__Ercolano_et_al._2008a_Instance_1

We use the set of wind solutions (density and velocity distribution of gas in the wind) for primordial discs (i.e. gas-rich, optically thick discs, which do not have an evacuated inner cavity) calculated by Owen et al. (2010, 2011) and EO10 for a 0.7 M⊙ star and X-ray luminosities (0.1 ≤ hν ≤ 10 keV) of LX = 2 × 1028, 2 × 1029 and 2 × 1030 erg s−1. These were obtained by means of two-dimensional hydrodynamic calculations using the zeus code (Stone & Norman 1992; Stone, Mihalas & Norman 1992; Hayes et al. 2006), modified to include the effects of X-ray irradiation with a parametrization of the gas temperature as a function of the local ionization parameter. The dust radiative transfer and photosionisation code mocassin (Ercolano et al. 2003; Ercolano, Barlow & Storey 2005; Ercolano et al. 2008a), modified according to Ercolano et al. (2008b), was used produce the temperature parametrization. The atomic data base of the mocassin code included opacity data from Verner et al. (1993) and Verner & Yakovlev (1995), energy levels, collision strengths and transition probabilities from Version 5.2 of the CHIANTI data base (Landi et al. 2006, and references therein) and hydrogen and helium free–bound continuous emission data of Ercolano & Storey (2006). The ionizing spectrum used to calculate the temperature parametrization was calculated by Ercolano et al. (2009), using the plasma code of Kashyap & Drake (2000) from an emission measure distribution based on that derived for RS CVn type binaries by Sanz-Forcada, Brickhouse & Dupree (2002), which peaks at 104 K and fits to Chandra spectra of T-Tauri stars by Maggio et al. (2007), which peaks at around 107.5 K. This spectrum has a significant EUV component (13.6 eV ≤ hν ≤ 0.1 keV), with roughly LEUV = LX. Solar abundances (Asplund, Grevesse & Sauval 2005), depleted according to Savage & Sembach (1996) were assumed, namely (number density, with respect to hydrogen): He/H = 0.1, C/H = 1.4 × 104, N/H = 8.32 × 105, O/H = 3.2 × 104, Ne/H = 1.2 × 104, Mg/H = 1.1 × 106, Si/H = 1.7 × 106, S/H = 2.8 × 105. More details about the codes and setup of the models can be found in Ercolano et al. (2008b, 2009) and Owen et al. (2010).

2019MNRAS.487.1626Q__Merloni,_Fabian_&_Ross_2000_Instance_1

Low-mass X-ray binaries (LMXBs) which either contain a black hole (BH) or a neutron star (NS), accreting matter from its low-mass companion star (≲1M⊙) are ideal natural laboratories for studying the physics of accretion and jet around a BH or an NS (e.g. Migliari & Fender 2006). According to the timing and spectral features in the X-ray band, LMXBs are generally divided into two main spectral states, i.e. the high/soft state and the low/hard state (Gilfanov 2010, for review). For BH-LMXBs, when they are in the high/soft state, the accretion flow is widely believed to be dominated by the optically thick, geometrically thin, cool accretion disc (Shakura & Sunyaev 1973), and the X-ray spectrum can be well described by a multicolour blackbody spectrum (e.g. Mitsuda et al. 1984; Makishima et al. 1986; Merloni, Fabian & Ross 2000). Whereas, for NS-LMXBs, besides the emission from the disc, there is a significant thermal emission from the boundary layer between the accretion disc and the surface of the NS (Popham & Narayan 1992; Inogamov & Sunyaev 1999; Popham & Sunyaev 2001; Gilfanov & Sunyaev 2014, for review). Observationally, for both BH-LMXBs and NS-LMXBs, when they are in the low/hard state, generally, the accretion flow is suggested to be dominated by the optically thin, geometrically thick, hot, advection-dominated accretion flow (ADAF) (Done, Gierliński & Kubota 2007, for review). Theoretically, the ADAF solution has been studied in detail by several researchers since it was discovered in 1970’s (Ichimaru 1977; Rees et al. 1982; Narayan & Yi 1994, 1995a,b; Abramowicz et al. 1995; Chen et al. 1995; Yuan & Narayan 2014, for review). In the BH case, the ADAF solution is a kind of radiatively inefficient accretion flow, in which a fraction of the viscously dissipated energy will be advected into the event horizon of the BH. While in the NS case, the viscously dissipated energy advected onto the surface of the NS will eventually be radiated out, so the ADAF solution is radiatively efficient (Narayan & Yi 1995b). Qiao & Liu (2018b) calculated the structure and the corresponding emergent spectrum of the ADAF around a weakly magnetized NS within the framework of the self-similar solution of the ADAF. The authors compared the electron temperature of the ADAF around a NS and a BH, it is found that the electron temperature of the ADAF around an NS is systemically lower than that of a BH, which is consistent with observations (Burke, Gilfanov & Sunyaev 2017; Qiao & Liu 2018b). Meanwhile, the authors compared the Compton y-parameter (defined as $y={{4kT_{\rm e}}\over {m_{\rm e}c^2}} \rm {Max}(\tau _{\rm es}, \tau ^2_{\rm es})$, with Te being the election temperature, me being the electron mass, c being the speed of light, and τes being the Compton scattering optical depth) of the ADAF around an NS and a BH, it is found that the Compton y-parameter of the ADAF around an NS is systemically lower than that of a BH, producing a softer X-ray spectrum, which is also consistent with observations (Wijnands et al. 2015; Parikh et al. 2017; Sonbas, Dhuga & Göğüş 2018; Qiao & Liu 2018b).

2019AandA...625A.121M__Beaugé_&_Nesvorný_2012_Instance_1

The final location of close-in giant planets in our models reflects the strength of the tides that we include in our modeling, which play a very important role in the decay of planetary orbits. These are dynamical tides (e.g., Lai 1997; Ivanov & Papaloizou 2004, 2007, 2011) and in our simulations we used a formulation given by Ivanov & Papaloizou (2007) as described inSect. 2. However, the impulse approximation used in the evaluation of dynamical tides becomes a poor approximation when the circularization proceeds (e.g., Mardling 1995a,b) and the eccentricity becomes low. Equilibrium tides become then effective (e.g., Beaugé & Nesvorný 2012 and references therein) and the tidal evolution may occur on a longer timescale. In short, at the beginning of the orbital evolution that leads to the formation of hot/warm Jupiters, dynamical tides are important in forcing the decay of the orbit. In the last part of the dynamical evolution when the eccentricity has become low, equilibrium tides are more important in determining the location where the planet stops. Unfortunately, at present it is not known when and how the two tides switch. When we change the magnitude of two tides, the final location of the planets can be adjusted (Beaugé & Nesvorný 2012). However, rather than introducing artificial effects, we continue to use dynamical tides in our simulations even for low eccentricities but we stop our simulations when the energy, decreasing from the tide at the pericenter, overcomes the orbital energy leading to a clustering of tidally circularized planets around 0.02 au. However, the final distribution of the inclination of the planets does not depend on this choice and highly misaligned planets would be produced anyway. Since the tidal evolution of planets with arbitrary inclinations is still not well known, we assume that planetary inclination is not significantly changed during tidal evolution (Barker & Ogilvie 2009). Thus, the planets maintain the inclination they have when the circularization begins.

2021MNRAS.508.4332M__Sickafoose_et_al._2001_Instance_1

As a first step, we need to specify the photoelectron sheath features. In this course, we first evaluate the steady state potential over the lunar surface (equation 4), and then after, we use this as a boundary condition to solve the Poisson equation (equation 2) and estimate the photoelectron sheath profile. In calculations, Lyman α (λ ∼ 121.57 nm, 10.29 eV, Λ ∼ 3 × 1011 cm–2 s–1) spike of solar photon radiation (Bauer 1973) is considered as the dominant source for the generation of photoelectrons from the lunar surface. The work function of the regolith material is taken from Grobman & Blank (1969), where it is suggested to vary in the range ϕr ∼ (4–6) V for the region across the subsolar point and limb. Moreover, Draine’s formulation is accounted to determine the lunar surface’s photoelectric efficiency (Draine 1978; Draine & Salpeter 1979) – its spectral dependence can be represented as ${\chi _{\nu r}} = {\chi _o}[1 - ({\phi _r}/{E_\nu })]$. For instance, for the Lyman α radiation χνr = 0.042 for optimum efficiency χo = 0.1 (Sickafoose et al. 2001) and ϕr = 6 V (Grobman & Blank 1969). Another significant parameter is the surface temperature which describes the electron population within the lattice available for the photoemission. Lunar Reconnaissance Orbiter based measurements (Williams et al. 2017) suggest that the surface temperature may vary from the equator (∼400 K) to the terminator (poles, ∼150 K). In order to take this account, we use the latitude (θ) dependent empirical relation ${T_\theta } = {T_0}[1 - (5/4\pi )\theta ]$; for instance, at θ = 70°, and To ≈ 205 K. These three parameters, viz., ϕr, To, χν, and Λ drive the photoemission current from the lunar regolith. The nominal solar wind plasma parameters are considered for calculating collection current over lunar regolith; the constituents are considered as40-41nes ≈ nis = 8.7 cm–3 and Tes ≈ Tis = 1.4 × 105 K (Mann et al. 2011; Kureshi et al. 2020). These solar radiation and wind plasma parameters might vary widely during active solar events and alter surface charging and sheath features. Popel et al. (2018) suggest the dust number density may also vary in a wide range depending on lunar altitude and particle size; for instance, nd ∼ 800 cm–3 for the particles of size 100 nm ≤ ao ≤ 200 nm and θ = 77°. Note that the secondary electron emission (Seitz 1940; Misra, Mishra & Sodha 2013) from the lunar regolith (and floating dust) is ignored, as it minimally contributes to the charging of sunlit surfaces (Mishra & Bhardwaj 2020). These parameters, along with equation (4), yield steady state potential over the sunlit locations. This estimate of the surface potential is used as a boundary condition (i.e. at l = 0, υ = υo) alongwith υ’ = υ = 0 as $l \to \infty $ to solve the Poisson equation (equation 2) numerically – using this framework, the sheath structure is derived in terms of electric potential (υ), electric field (Es), and photoelectron population density (npe).

2019ApJ...883..130L__Hansen_2009_Instance_1

(3) What is the likelihood of obtaining Mercury and Mars analogs in systems with Venus–Earth pair analogs? Recent terrestrial planet formation studies managed to statistically produce low-mass Mars analogs. In particular, there are currently five main competing models. (1) Grand Tack: the protoplanetary disk is truncated at ∼1 au by perturbations of an inward-then-outward gas-driven migrating Jupiter within the first 1–3 Myr of the solar system history, leaving a disk with mass concentrated within that distance after the disk gas dispersal (Walsh et al. 2011; Jacobson & Morbidelli 2014; Walsh & Levison 2016; Brasser et al. 2016a); (2) Empty Asteroid Belt: embryos and planetesimals in the protoplanetary disk formed concentrated within a narrow belt at ∼0.7–1 au (Hansen 2009; Drazkowska et al. 2016; Raymond & Izidoro 2017; Ogihara et al. 2018); (3) Early Instability: the protoplanetary disk is perturbed by the giant planets’ instability that occurred within ∼10 Myr after the disk gas dispersal, strongly depleting the disk mass beyond ∼1.3 au (Clement et al. 2018, 2019b); (4) Pebble Accretion: embryos and planetesimals form preferentially in the inner and outer regions of the protoplanetary disk, respectively, and the disk mass is concentrated within ∼1.5 au (Levison et al. 2015; Chambers 2016); (5) Sweeping Secular Resonance: the growth of embryos and planetesimals is inhibited beyond ∼1–1.5 au by perturbations of secular resonances that swept the disk during the disk gas dispersal (Bromley & Kenyon 2017). In all these models, Mars analogs may form as a result of the disk mass depletion or absence of mass beyond ∼1–1.5 au. Additionally, from the standpoint of the initial conditions of the disk after gas dispersal, models 1 and 2 are very similar, while models 3–5 probably also share similar properties. However, in the majority of those studies, it remains unclear whether these Mars analogs were obtained in systems that also contained Venus and Earth analogs. Moreover, even if they were, it is also unclear what fraction these three-planet analog systems would represent compared to all the systems obtained in those studies, because their results are often presented with all planets mixed (e.g., in plots of distance vs. mass). Furthermore, Mercury and Mars analogs in those studies are often defined as planets from the mixed population that satisfy an arbitrary distance range: e.g., 0.5 au and 1.2–2.0 au, respectively. However, this approach can lead to incomplete classifications in a given system (e.g., by failing to identify more massive Mars analogs at 1.2 au or failing to properly identify planet analogs). Indeed, it is difficult to discriminate Venus from Earth analogs, unambiguously identify Mercury/Mars analogs, and avoid misclassifications in past studies. If the five models described above are successful in reproducing a low-mass Mars, how can the best among them be discriminated without a proper system classification?

2017AandA...605A.121M__Thompson_et_al._2017_Instance_1

The effects of the wet air, the variable water vapour cells, are the main cause of the refraction at submillimetre/millimetre wavelengths. The dipole moment of water makes water vapour, the wet component in the troposphere, a strong absorber at submillimetre/millimetre wavelengths and significantly increases the refractive index of the air. Because the water vapour is not well mixed there are localised pockets of air with different refractive indices. In what is called the “frozen-screen” hypothesis (Taylor 1938), these pockets, or turbulent eddies, are assumed to be fixed in the atmospheric layer that advects over an interferometric array (Thompson et al. 2017). Thus, these cause various delays in the path length (variable in time and position) along the line of sight to each antenna. Interferometers are sensitive to the variations in path length, the interferometric phase difference, between pairs of antennas that form a baseline. For a given baseline (distance and orientation) the line-of-sight path to each component of an astronomical source has an intrinsic phase that relates the measured intensities to their location in an image. Thus any additional variable atmospheric delays that cause anomalous phase changes on many baselines making up an array have the effect of blurring the interferometric image; this is analogous to the effect of seeing at optical and infrared wavelengths. The introduced delays scale linearly with the difference in precipitable water vapour (ΔPWV) between an antenna pair (excluding dispersive effects) and linearly with frequency. The correlated signals between pairs of antennas (the visibilities V = V0eiφ) become partly decorrelated as a result of the phase noise. The reduced coherence for the visibilities is given by (1)\begin{equation} \label{eqn0} \langle V \rangle = V_0 \times \langle {\rm e}^{\rm i\phi} \rangle = V_0 \times {\rm e}^{-\phi^2_{\rm rms}/2} , \end{equation}⟨V⟩=V0×⟨eiφ⟩=V0×e−φrms2/2,

2021AandA...653A.154T__VI_2020_Instance_1

The global volume-weighted neutral fraction of hydrogen QH I in the high-resolution volume is presented as the thick blue line in Fig. 10. The reionization process starts when the first stars are born, and by z50 ≃ 7.63, half of the volume is reionized. We identify the redshifts at which 1%, 10%, 50%, 90% and 99% of the volume is ionized as z01 = 11.13, z10 = 8.68, z50 = 7.63, z90 = 6.58, and z99 = 5.92 respectively; corresponding to a reionization duration Δz = z99 − z10 = 2.8 (Δt ≃ 385 Myr), broadly consistent with the estimates of Robertson et al. (2015) The dark and light shaded areas in Fig. 10 correspond to the 1σ and 2σ constraints on the redshift of reionization from the cosmic microwave background measurements of the Planck mission (Planck Collaboration VI 2020), with a reionization midpoint zre = 7.67 ± 0.73. We also compare the OBELISK reionization history to a selection of observational constraints: Black hexagons correspond to the measurements of the Lyman-α forest transmission (Lyα forest, Fan et al. 2006b), the green circles show constraints on the IGM opacity from the fraction of Lyman-α emitters in Lyman-break galaxy samples (Schenker et al. 2014; Ono et al. 2012; Pentericci et al. 2014; Robertson et al. 2013; Tilvi et al. 2014), the purple diamonds show measurements from quasar damping wings by Mortlock et al. (2011), Schroeder et al. (2013), Bañados et al. (2018), Ďurovčíková et al. (2020), the red diamonds show similar measurements on gamma-ray bursts (GRB, Totani et al. 2006, 2016), and the black squares from Ouchi et al. (2010), Ota et al. (2008) represent constraints derived from the evolution of the Lyman-α luminosity function. Some of these data points come from the compilations of Bouwens et al. (2015). Overall, we find that the simulation agrees with most observations in terms of reionization history, despite the fact that we focus on an overdense region. Interestingly, the simulation manages to capture residual neutral fraction after reionization is complete at z  6 similar to what is observed. We discuss this point further below.

2022AandA...666L...5G__Esparza-Arredondo_et_al._2018_Instance_1

More recently, García-Bernete et al. (2022) found that the PAH molecules responsible for the 11.3 μm PAH emission band are more resilient in the hard environments often present in AGN. In particular, the authors found larger 11.3/7.7 μm and 11.3/6.2 μm PAH ratios in AGN-dominated systems compared to SF galaxies, indicating a larger fraction of neutral PAH molecules (as noted by Smith et al. 2007 using a sample of relatively weak AGN). However, these studies were limited by the spatial resolution (∼4″) and the low spectral resolution (R ∼ 60–130) of Spitzer/InfraRed Spectrograph (IRS). Previous sub-arcsecond angular resolution N-band (∼8–13 μm) ground-based spectroscopic studies investigated the 11.3 μm PAH feature in the nuclear and circumnuclear regions of AGN (e.g., Hönig et al. 2010; González-Martín et al. 2013; Alonso-Herrero et al. 2014, 2016; Ramos et al. 2014; Esquej et al. 2014; García-Bernete et al. 2015; Jensen et al. 2017; Esparza-Arredondo et al. 2018). However, these works were unable to provide definitive details regarding the effect of the AGN on the PAH molecules due to limited wavelength coverage and sensitivity. The changes in the PAH properties due to the presence of the AGN might be more prominent in their innermost regions of galaxies. Therefore, the unprecedented combination of high angular and spectral resolution (R ∼ 1500 − 3500) in the entire mid-IR range (4.9–28.1 μm) afforded by the James Webb Space Telescope (JWST)/Mid-Infrared Instrument (MIRI; Rieke et al. 2015; Wells et al. 2015; Wright et al. 2015) is key to investigating PAH properties. In this Letter we report on the first investigation of PAH emission in the nuclear regions of three luminous Seyfert (Sy) galaxies and compare them with emission from SF regions using JWST/MIRI Medium Resolution Spectrograph (MRS) data. This enables us, for the first time, to characterise the PAH properties of local luminous Sy galaxies (log (Lbol)> 44.46 erg s−1)1 at sub-arcsecond scales (∼0.45″, ∼142–245 pc).

2021AandA...654A..80S__Urrutia_et_al._2019_Instance_1

In this study we are focusing on rest-frame UV emission red-wards of the strongest UV emission line, Lyα, partially motivated by the challenges of observing this line at high redshift (z ≳ 6) where the significantly neutral CGM and IGM absorbs the Lyα photons escaping the galaxy along the line of sight (e.g., Dijkstra et al. 2011; Laursen et al. 2011, 2019; Dijkstra 2017). Nevertheless, the Lyα line itself has improved our understanding of star-forming galaxies in the (early) Universe. In particular, the asymmetric Lyα line profile has enabled redshift confirmations of large samples of sources at both 2  z  6 (e.g., Steidel et al. 2014; Le Fevre et al. 2015; Herenz et al. 2017; Inami et al. 2017; Urrutia et al. 2019) and high redshift at z > 6 (e.g., Finkelstein et al. 2013; Oesch et al. 2015; Schmidt et al. 2016; Tilvi et al. 2016; Huang et al. 2016; Pentericci et al. 2018; Fuller et al. 2020). The resonant scattering of the photons and the resulting (occasional) multipeaked emission has been shown to relate closely to the column density and dynamics of the neutral hydrogen in the ISM and the CGM (Verhamme et al. 2015; Gazagnes et al. 2018, 2020). The fraction of galaxies with confirmed Lyα emission has been used to probe the evolution (or lack thereof) of the fraction of LAEs among Lyman-break galaxies from low redshift to the EoR (e.g., Treu et al. 2013; Pentericci et al. 2014; Tilvi et al. 2014; de Barros et al. 2017; Caruana et al. 2018; Kusakabe et al. 2020). Together with the observed velocity offset of the Lyα line resulting from resonant scattering (Schenker et al. 2013; Erb et al. 2014; Hashimoto et al. 2015; Stark et al. 2017; Verhamme et al. 2018), this has probed the amount of neutral gas in the IGM and has constrained the neutral fraction of the Universe during the EoR (Ouchi et al. 2010; Greig et al. 2017; Mason et al. 2018a,b; Banados et al. 2018; Hoag et al. 2019). Furthermore, comparisons between Lyα and Hα or UV emission line strengths have been used to study the production efficiency and escape of ionizing photons from LAEs (Nakajima et al. 2016; Matthee et al. 2017; Harikane et al. 2018; Lam et al. 2019; Maseda et al. 2020). It is therefore of interest to relate and compare the measured rest-frame UV emission lines red-wards of Lyα studied here, with the characteristics of the Lyα line itself and the properties of the LAEs in our sample.

2022ApJ...938..124Z__Tu_&_Marsch_1995_Instance_1

In the context of solar wind turbulence, one crucial question concerns turbulence evolution and heating (Parashar et al. 2015; Chen 2016; Viall & Borovsky 2020). Numerous correlations exist between solar wind parameters and magnetic fluctuations, which can provide important indications and constraints on the physics of turbulence evolution and heating processes. Early observations showed that faster solar wind usually corresponds to higher proton temperature (e.g., Marsch et al. 1982; Elliott et al. 2012). The faster solar wind tends to be more imbalanced with greater wave energy flux antisunward than sunward (Tu & Marsch 1995), and can be characterized by larger inertial-scale magnetic energy density and steeper proton-scale magnetic spectrum (Bruno et al. 2014). A study by Wind observations revealed that higher proton temperature is associated with a steeper proton-scale magnetic spectrum (Leamon et al. 1998b). Statistical studies by Wind and Advanced Composition Explorer measurements later revealed that higher proton temperature is related to larger inertial-scale magnetic energy density (Smith et al. 2006; Vech et al. 2018). Recent statistical studies by Wind and Parker Solar Probe (PSP) observations further revealed that higher proton temperature is linked to larger proton-scale magnetic energy density (Zhao et al. 2020, 2022). On the other hand, cross and magnetic helicities at inertial and kinetic scales, respectively, are often employed to describe the imbalance and handedness of solar wind turbulence. A correlation between cross and magnetic helicities was reported, which was interpreted as the signature of cyclotron-resonant dissipation in solar wind turbulence (Leamon et al. 1998a). A strong correlation (with a correlation coefficient (CC) up to 0.8 occasionally) between the proton-scale spectral index and magnetic helicity was found (Pine et al. 2020; Zhao et al. 2021, 2022). Via PSP observations, a mild correlation (with a CC of 0.36) between the spectral index and cross helicity was displayed in the most recent literature (Huang et al. 2021). However, less attention has been paid to the relations between these correlations despite the large body of research.

2022ApJ...940....5D__Bálazs_et_al._2003_Instance_1

Figure 1 shows the best fits of a multiple-Gaussian model to the T 90 distributions of distinct Swift/BAT GRB samples. Interestingly, we find from Figures 1(a)–(d) that the lognormal T 90 durations of all Swift GRBs except those with good spectra in sample V are triply distributed. It is confirmed in Figure 1(e) that the Swift/BAT GRBs with a well-measured peak energy are bimodally distributed at a boundary of ∼1 s, which is in good agreement with Zhang et al. (2020). Note that the third component remains controversial. On the other hand, Hakkila et al. (2000) suggested that the third subgroup proved by statistics was only a deviation caused by complex instrumental effects, which could reduce the duration of some weak long pulses. In addition, another classification scheme uses a scatter plot of flux and duration fitted with two-dimensional Gaussian functions (Bálazs et al. 2003). Some authors pointed out that there are more than two clusters (Mukherjee et al. 1998; Horváth 1998). Unfortunately, the physical origin of the extra components cannot be reasonably interpreted. The two-Gaussian fit to data in Figure 1(e) demonstrates that the duration distribution peaks at 0.21 ± 0.38 s with a spread of 0.93 dex for short bursts and at 43.97 ± 1.05 s with a spread of 1.12 dex for long GRBs. The best fit returns a good reduced Chi-square of χν2 ≈ 0.82, indicating that two classes are evidently reconfirmed and separated at T 90 ≈ 1.06 s instead of at T 90 = 2 s shown by CGRO/BATSE data. The dividing line of 1 s is consistent with some previous results of Swift/BAT GRBs (Berger et al. 2013; Gruber et al. 2014; Zhang et al. 2020). Additionally, the number of components in each T 90 distribution of Figure 1 has been accordingly tested by the traditional Bayesian information criterion (BIC) as we previously did (e.g., Zhang et al. 2016; Li et al. 2021b). Figure 1 demonstrates that the S/N level and the EE component are two important contributors to the third class of GRBs. In other words, one of them can separately confuse the classification of GRBs in terms of T 90 only.

2020MNRAS.495.4508E__Heinke_et_al._2014_Instance_3

Several qLMXBs have been identified in GCs and in the Galactic field (for some examples, see table 4 in Guillot et al. 2009 and references therein). While LMXBs in the field were detected following the onset of a bright accretion outburst, most qLMXBs in GCs, including all those with the highest flux at Earth, have not shown accretion activity.3 Most of these sources have only been spectrally identified based on their similarities to field LMXBs, observed during quiescence (e.g. Cen X-4 or Aql X-1). Previous works have confirmed that H-atmosphere models accurately describe the spectra of qLMXBs, with radii in the range 10–15 km, as expected for NSs, either from single sources (e.g. Heinke et al. 2006a; Webb & Barret 2007; Guillot, Rutledge & Brown 2011; Heinke et al. 2014; Bogdanov et al. 2016), or from statistical analyses of multiple qLMXBs (e.g. Guillot et al. 2013; Guillot & Rutledge 2014; Lattimer & Steiner 2014; Guillot 2016; Steiner et al. 2018). However, in some cases the accreted material may not be hydrogen, but helium (e.g. Servillat et al. 2012; Catuneanu et al. 2013; Heinke et al. 2014). One way to circumvent this is to identify the nature of the donor star, i.e. to determine the nature of the material transferred on to the NS (e.g. with the detection of an H α emission line, presumably originating in a faint accretion disc, Haggard et al. 2004). The possibility of helium (or heavier element) atmospheres is well-founded on the existence of ultracompact X-ray binaries (UCXB), with white dwarfs or helium-dominated donors4 (e.g. Zurek et al. 2009; Altamirano et al. 2010; Sanna et al. 2017; Cadelano et al. 2019). In fact, around 1/3 of the LMXBs in GCs with constraints on the companion nature, possess a white dwarf donor (Bahramian et al. 2014). Since NS He-atmosphere models have harder spectra than H-atmosphere models, using the incorrect composition for the observed thermal emission can result in biases of the inferred radii (Servillat et al. 2012; Heinke et al. 2014).

2022ApJ...924...56S__Schreiber_et_al._2015_Instance_1

The second ingredient is the probability distribution of stellar mass at given SFR and redshift: 5 dpdlogM⋆(M⋆∣ψ,z)∝M⋆M⋆M⋆,MS(ψ,z)M⋆,MSexp−logM⋆−logM⋆,MSψ,z22σlogM⋆2M⋆≥M⋆,MS(ψ,z), where M ⋆,MS(ψ, z) is the observed redshift-dependent galaxy main sequence with log-normal scatter σlogM⋆≈0.2 dex (we adopt the determination by Speagle et al. 2014 for an anlytic fit, see their Equation (28)). The main sequence is a relationship between SFR and stellar mass followed by the majority of star-forming galaxies, apart from some outliers located above the average SFR at given stellar mass (see Daddi et al. 2007; Rodighiero et al. 2011, 2015; Sargent et al. 2012; Speagle et al. 2014; Whitaker et al. 2014; Schreiber et al. 2015; Caputi et al. 2017; Bisigello et al. 2018; Boogaard et al. 2018). The expression in Equation (5) holds for an approximately constant SFR history, which is indicated both by in situ galaxy formation scenarios (see Mancuso et al. 2016b; Pantoni et al. 2019; Lapi et al. 2020) and by observations of ETG progenitors (that have on overage slowly rising star formation history with typical duration of ≲1 Gyr; see Papovich et al. 2011; Smit et al. 2012; Moustakas et al. 2013; Steinhardt et al. 2014; Cassará et al. 2016; Citro et al. 2016) and late-type galaxies (that have on the average slowly declining star formation history over a long timescale of several gigayears; e.g., see Chiappini et al. 1997; Courteau et al. 2014; Pezzulli & Fraternali 2016; Grisoni et al. 2017). In this vein, off-main-sequence objects can be simply viewed as galaxies caught in an early evolutionary stage that are still accumulating their stellar mass (which grows almost linearly with time for a constant SFR), and are thus found to be preferentially located above the main sequence or, better, to the left of it. As time goes by and the stellar mass increases, the galaxy moves toward the average main-sequence relationship, around which it will spend most of its lifetime before being quenched due to gas exhaustion or feedback processes.

2018ApJ...853..148C__Shibuya_et_al._2014_Instance_2

LAE galaxies are defined by a high equivalent width (EW > 20 Å) Lyα line and are believed to be composed of extremely large regions of active star formation. Many efforts have been made to detect and characterize LAE galaxies (e.g., Conselice et al. 2003; Conselice 2004; Ravindranath et al. 2006; Shimasaku et al. 2006; Bournaud et al. 2007; Ouchi et al. 2008, 2017; Elmegreen et al. 2009a, 2009b; Tacconi et al. 2010; Gronwall et al. 2011; Kashikawa et al. 2011; Mandelker et al. 2014; Moody et al. 2014; Guo et al. 2015). In general, these galaxies appear as clusters of bright clumps, sometimes with a background of continuum emission. Evidence suggests that these clumps are larger and brighter than most star-forming regions in nearby low-redshift galaxies (Elmegreen et al. 2009a). Efforts have been made in quantifying mass, star formation rates, gas composition, and kinematics, as well as other LAE properties (e.g., Nilsson et al. 2009; Ono et al. 2010a, 2010b; Swinbank et al. 2010; Tacconi et al. 2010; Shibuya et al. 2014; Livermore et al. 2015; Nakajima et al. 2016; Hashimoto et al. 2017). These have revealed a wealth of information about the early universe, but they are ultimately limited by LAE surface brightnesses. Most studies rely upon stacks of galaxies and can draw only limited inferences about individual LAEs. Other studies show that LAE dust content, particularly clumpy dust, in the interstellar medium (ISM) can have an impact on most LAE observables (Kobayashi et al. 2007, 2010; Verhamme et al. 2008; Duval et al. 2014). Finkelstein et al. (2009) showed that clumpy dust models can provide a good fit to a set of z ∼ 4.5 LAEs, although they invoked a multiphase ISM that may be unlikely to form in nature (Laursen et al. 2013). Nevertheless, dust in LAE galaxy ISM could cause some of the irregularity in LAE surface-brightness profiles (Buck et al. 2017). With limited resolution, however, it is difficult to make this distinction. A further challenge to morphological studies is that the clump sizes are near the resolution limit of instrumental point spread functions (PSFs) and often cannot be distinguished from point sources (Guo et al. 2015). As a result, direct imaging studies cannot decisively determine whether the clumps are different in nature from star-forming regions in our local universe or if the larger apparent size is merely an artifact of insufficient resolution (Shibuya et al. 2014; Kobayashi et al. 2016; Tamburello et al. 2017; Fisher et al. 2017).

2020ApJ...895L...8R__Kapferer_et_al._2009_Instance_1

As clusters of galaxies assemble, they dynamically transform the physical properties of in-falling cluster members. Galaxies falling into a cluster experience ram pressure from dense intracluster medium (ICM) gas that can potentially unbind their individual gas reservoirs (Gunn et al. 1972). This process referred to as ram pressure stripping (RPS) can eventually remove a galaxy’s entire gas supply, making it an important quenching pathway for satellite galaxies (Vollmer et al. 2001; Tonnesen et al. 2007). Observationally, RPS results in disturbed galaxy morphologies and trailing tails of stripped gas (e.g., Kenney et al. 2004; van Gorkom 2004; Cramer et al. 2019). The most extreme examples have been dubbed “jellyfish” galaxies, due to the evocative morphologies of their star-forming tails (Ebeling et al. 2014; Boselli et al. 2016; Poggianti et al. 2016). Prior to complete gas removal, moderate values of ram pressure have also been shown to increase the star formation rate in galaxies both observed (Crowl & Kenney 2006; Merluzzi et al. 2013; Vulcani et al. 2018) and simulated (Kronberger et al. 2008; Kapferer et al. 2009; Tonnesen & Bryan 2009; Bekki 2014). In this picture, the increased pressure initially helps compress the gas and triggers increased star formation. Other proposed processes for accretion of gas to the centers of galaxies include gravitational instabilities and the inspiral of preferentially low angular momentum clumps that lose angular momentum to a wind: drag from the nonrotating ICM operating on dense clumps (Schulz & Struck 2001; Tonnesen & Bryan 2009; Ramos-Martínez et al. 2018). Over time, the interstellar medium (ISM) is fully stripped from the galaxy and star formation ceases. Recently, a very high incidence of AGN (5/7) has been observed in a sample of jellyfish galaxies (Poggianti et al. 2017), and comprehensive follow-up of this sample has led to the identification of AGN-driven outflows (Radovich et al. 2019) and a compelling case for AGN feedback in action (George et al. 2019). It is plausible that the same mechanisms that initially promote star formation can also fuel active galactic nuclei (AGN) during the ram pressure stripping process. Indeed, outside of cluster environments, ram pressure induced shocks are known to produce nuclear inflows that can fuel AGN in the context of galaxy merger simulations (Barnes 2002; Capelo & Dotti 2017; Blumenthal & Barnes 2018).

2016MNRAS.461.3982B__Walsh_&_Richardson_2008_Instance_1

Many studies have been done to understand the dynamics and origin of such systems since the discovery of the first binary asteroid system, Dactyl orbiting around (243) Ida in 1993 (Chapman et al. 1995). Based on the structure of ‘rubble pile’ asteroids (a collection of gravitationally bound boulders with a distribution of size scales and very little tensile strength between them), a model for how they can disrupt due to close flybys of a planet was developed. However, close encounters with the planets proved not to be enough for creation of the current population of binary systems (Margot et al. 2002; Walsh & Richardson 2008). Another model for their formation is by increasing their spin rates due to incident and remitted solar photons, known as the Yarkovsky–O'Keefe–Radzievskii–Paddack (YORP) effect. The YORP effect on contact binary asteroids has been studied (Bottke et al. 2002; Merline et al. 2002; Scheeres 2002; Walsh & Richardson 2006). Using a model with an ellipsoid and a sphere in a planar case, Scheeres (2007) studied fission limits (spin limit to occur a fission) and the stability of that kind of system for different initial conditions. After that, the stability of a binary system was analysed using a two-ellipsoid model (Scheeres 2009). Pravec et al. (2010) made a complete study about formation of asteroid pairs through rotation fission. Jacobson & Scheeres (2011) studied the creation of binaries and other observed near-Earth asteroid (NEA) systems, including doubly synchronous binaries, high-e binaries, ternary systems and contact binaries. That study analysed the dynamics of a binary system just after rotational fission. Using a two-ellipsoid model taking into account mutual gravitational interactions and tidal dissipation, they analysed the dynamics for different mass ratios of the system under a planar assumption. The current work follows from these results, but looks at more likely, non-planar initial configurations. This extension is significant, as non-planar cases must take into account the complete rotational motion (rotation, precession and nutation) of each body. Our results are compared with the results obtained by Jacobson & Scheeres (2011).

2020MNRAS.496.5528M__Greif_et_al._2012_Instance_1

Theoretically predicted magnetic field in the formation of the first stars. The evolution of a dynamo in a collapsing minihalo depends on a large number of parameters: the initial density, nH,0, the turbulent velocity, vt (which we parametrize in terms of the virial velocity, vt = ϕtvvir), the temperature, T, the mass of the collapsing cloud, M0, the rate of collapse (parametrized by ϕff), and the rate at which these quantities vary with density (denoted by qx for quantity x). (The initial value of the field, B0, enters only logarithmically, and is important only if it is many orders of magnitude less than our estimate of ∼10−16 G.) Choosing values of these parameters that are consistent with simulations (e.g. those of Greif et al. 2012), we find that the time for the field to grow from its initial amplitude of ∼10−16 G to equipartition at the viscous scale, Bν ∼ 10−8 G, is less than the virial time in the minihalo; hence, the exponential growth of the field occurs at approximately constant gas density. This rapid growth of the field is consistent with that found in previous work (e.g. Schleicher et al. 2010; Schober et al. 2012b). The subsequent non-linear dynamo amplification is sufficient to bring the field energy to within about an order of magnitude of equipartition; none the less, the overall amplification of the field is generally dominated by compression. We estimate that the field first reaches equipartition with turbulent velocities of the order of 2 km s−1 (taken from simulations) at a value of ∼10−4 G; the field subsequently grows as $n_{\rm H}^{1/2}$. The field reaches equipartition with the central 5 per cent of the mass of the gas. Our conclusion that the field reaches equipartition in a minihalo at z ∼ 25 differs from that of Xu & Lazarian (2016), who found that equipartition was not reached until a time of about 6 × 108 yr (the age of the Universe at z ≃ 8) since they did not consider the increase in density that occurs in star formation.

2021ApJ...913L..14H__Priest_&_Schrijver_2000_Instance_1

Thanks to the observations with high time resolution and high optical sensitivity from state-of-the-art facilities, various dynamic phenomena, and processes (e.g., magnetic reconnections, jet flows, and oscillatory waves) have been observed to be omnipresent in the multi-layers of solar atmosphere (Shibata et al. 2007; Tomczyk et al. 2007; He et al. 2010; Tian et al. 2014; Shen et al. 2018). In the past, magnetic reconnection and wave dissipation are viewed as two seemingly distinct mechanisms opposite to one another when dealing with the problems of coronal heating and solar wind origin (e.g., Cranmer & Van Ballegooijen 2010). Magnetic reconnection rapidly converts magnetic energy to particle energy, causing emission flare in multiple wave bands and even triggering coronal mass ejections (Priest & Schrijver 2000). Nano-jets are observed in closed loop system and have been suggested as being caused by the slingshot effect of newly reconnected magnetic field lines with a small angle of shear before reconnection (Antolin et al. 2021). Oscillatory wave signatures (e.g., oscillations in radiation intensity and Doppler velocities) have been considered as propagating or standing in both open strands or closed loops throughout the solar atmosphere (Wang et al. 2009). Flares are found to be often accompanied by quasi-periodic pulsations in multiband radiation intensities (Nakariakov et al. 2010; Van Doorsselaere et al. 2016; McLaughlin et al. 2018). The causal relation between reconnection and Alfvén waves was explored in 3D magnetohydrodynamic (MHD) simulations using the Walén test of the field-aligned current and parallel vorticity as emitting from the reconnection site (Ma et al. 1995). In the chromosphere, excitation of kink or Alfvénic waves, as well as compressive slow-mode waves, are also discovered to be associated with the formation of type-II spicules, which are launched by magnetic reconnection (He et al. 2009a, 2009b; Liu et al. 2014).

2018AandA...616A..99K__Narang_et_al._2016_Instance_3

The high-resolution imaging observations of TR from IRIS reveal the ubiquitous presence of network jets. We have used three different IRIS observations of the quiet sun, which are located near the disk center. On the basis of careful inspection, 51 network jets are identified from three QS observations and used for further analysis. These 51 network jets are very well resolved and are not affected by the dynamics of other jets. The study is focused on the rotating motion of network jets along with the estimation of their other properties (speed, height, and lifetime). The mean speed, as predicted by statistical distributions of the speed, is 140.16 km s−1 with a standard deviation of 39.41 km s−1. The mean speed of network jets is very similar, as reported in previous works (e.g., Tian et al. 2014; Narang et al. 2016). However, in case of their lifetimes, we found a value that is almost double (105.49 s) that of the previously reported mean lifetime of the network jets (49.6 s; Tian et al. 2014). As mentioned above, we took only those network jets that are very well resolved in space and in the time; these criteria exclude short lifetime network jets. Therefore, our statistical distribution of the lifetime predicts a higher mean lifetime. The mean length of the network jets is 3.16 Mm with a standard deviation of 1.18 Mm. In the case of CH network jets, Tian et al. (2014) have reported that most of the network jets have lengths from 4.0 to 10.0 Mm. However, the mean length for QS network jets is smaller (3.53 Mm; Narang et al. 2016). So, the mean length for QS network jets from the present work is in good agreement with Narang et al. (2016). In addition, the apparent speed and length of these network jets are positively correlated, which is very similar to what has already been reported in previous works (Narang et al. 2016). Finally, we can say that these networks jets are very dynamic features of the solar TR, as revealed by their estimated properties.

2022AandA...666A..51M__Djurašević_1993_Instance_1

RX Cas is part of a group of interacting binaries showing light curves similar to β Lyrae and showing a long photometric cycle of unknown origin (Mennickent 2017). Few of these interacting binaries have been studied in terms of their physical changes during the long cycle. Here we provide a brief comparison with some of them. In RX Cas, we find that the long cycle of around 516 days can be explained in terms of changes of the physical parameters of the accretion disk, which was mentioned along with other possible causes in the past (Kříž et al. 1980). The disk turns out to be hotter and thicker at long-cycle maximum, but it does not change appreciably in radial extension. We note that a hotter disk during maximum was predicted by a simpler study in the past (Djurašević 1993). Contrarily, large changes in disk radius and thickness have been inferred in OGLE-BLG-ECL-157529, a binary characterized by orbital and long periods of 24 . d $ \overset{\text{d}}{.} $ 8 and ∼850 days, while its long cycle can be reproduced by occultation of the gainer by a variable disk thickness (Mennickent & Djurašević 2021). On the other hand, the system OGLE-LMC-DPV-097 is characterized by a long-cycle length of ∼306 days and an amplitude of ∼0.8 mag in the I band. This binary has an orbital period of 7 . d $ \overset{\text{d}}{.} $ 75 and relatively low stellar masses of 5.5 and 1.1 M⊙. In this system the orbital light curve changes in a systematic way during the long cycle. At the minimum of the long cycle the secondary eclipse practically disappears and during the ascending branch the system is brighter in the first quadrant than during the second quadrant. The disk radius is 7.5 R⊙ at the minimum and 15.3 R⊙ at the ascending branch of the long cycle; its temperature at the outer edge changes from 6870 to 4030 K in these two stages (Garcés et al. 2018). OGLE-LMC-DPV-065 is another interesting binary with a large amplitude of the long cycle. The long cycle is characterized by a double hump light curve in the I and V bands whose general shape is nearly constant with only minor variations. After a continuous decrease in the long-period from 350 to 218 d lasting about 13 yr, the long cycle remained almost constant for about 10 yr. However, the orbital light curve is fairly constant in shape, and no clear link between the disk structure and long cycle has been observed for this system, in a clear contrast to OGLE-LMC-DPV-097 and RX Cas (Mennickent et al. 2019). From the above we conclude that we cannot draw general conclusions about disk changes and photometric long cycles in these few systems as they show different behaviors that cannot be easily adapted to a global single picture.

2018MNRAS.476.2591V__Nikolic,_Cullen_&_Alexander_2004_Instance_1

Galaxy interactions represent a fundamental component of our current view of hierarchical galaxy evolution. Studies based on both observations and simulations have shown that galaxy collisions and mergers can dramatically affect the galaxies undergoing the interaction, by, e.g. triggering nuclear activity (e.g. Kennicutt 1984; Kennicutt et al. 1987; Ellison et al. 2011, 2013a; Silvermann et al. 2011; Satyapal et al. 2014), producing colour changes (e.g. Larson & Tinsley 1978; Darg et al. 2010; Patton et al. 2011), disrupting morphologies (e.g. Kaviraj et al. 2011; Patton et al. 2016; Lofthouse et al. 2017), and altering the metallicities (e.g. Rupke et al. 2010; Perez, Michel-Dansac & Tissera 2011; Scudder et al. 2012; Torrey et al. 2012). The most evident effect driven by galaxy encounters is probably the triggering of new episodes of star formation, which can occur both in the pre-merger regime between first pericentre and coalescence (e.g. Nikolic, Cullen & Alexander 2004; Ellison et al. 2008, 2013b; Patton et al. 2011; Scudder et al. 2012), and in the post-merger phase, where the two nuclei of the interacting galaxies have merged together (e.g. Kaviraj et al. 2012; Kaviraj 2014; Ellison et al. 2013a). The idea that galaxy mergers have a strong impact on the star formation activity is supported by studies of Ultra-Luminous InfraRed Galaxies (ULIRGs), i.e. galaxies with IR luminosities exceeding 1012 L⊙ and characterized by star formation rates (SFRs) up to ∼1000 M⊙ yr−1 (e.g. Barnes & Hernquist 1991; Mihos & Hernquist 1994; Daddi et al. 2010; Scoville et al. 2015). Observations have revealed that the majority of ULIRGs reside in interacting systems (e.g. Sanders & Mirabel 1996; Veilleux, Kim & Sanders 2002; Kartaltepe et al. 2010, 2012; Haan et al. 2011). Nevertheless, ULIRGs are rare and extreme examples of highly star-forming galaxies. Most galaxy–galaxy interactions result in SFR increases of at most a factor of a few, as shown in both numerical simulations (e.g. Di Matteo et al. 2008) and observations of galaxy pairs and post-mergers (Ellison et al. 2008; Martig & Bournaud 2008; Jogee et al. 2009; Robaina et al. 2009; Scudder et al. 2012).

2022MNRAS.516.3532M__Johnson_et_al._2017_Instance_1

The size–SFR relation has been already well-defined in the local Universe investigating H ii regions in nearby spiral and irregular galaxies by Kennicutt (1988). On the other hand, outliers are mainly hosted by interacting systems as shown in the case of the Antennae galaxy (Bastian et al. 2006), and have been thoroughly investigated on a larger samples with DYNAMO (Green et al. 2014) and LARS (Messa et al. 2019). In Fig. 10, we show the relation between the size and star formation rate of clumps, including samples across different redshifts (Swinbank et al. 2009, 2012; Jones et al. 2010; Livermore et al. 2012, 2015; Johnson et al. 2017). We consider samples where SFRs was estimated through SED fitting (as for our sample) or from H α, using the prescription by Kennicutt (1988). Our sample and the Sunburst clumps (Vanzella et al. 2021a), despite being at z ∼ 2–6, have sizes comparable to local H ii regions and GCs (Swinbank et al. 2009; Jones et al. 2010; Livermore et al. 2012; Johnson et al. 2017), but they have SFRs ∼300 times higher. They also have SFRs ∼100 times higher than clumps from Wuyts et al. (2014) and Livermore et al. (2015), which are part of a sample of lensed systems at z > 2. In particular, we compare our sample with two well-known H ii regions from the local Universe: 30 Doradus and II Zw40 (Vanzi et al. 2008). One possible explanation for the high SFR measured in our sample is enhanced interactions, or larger gas reservoirs at high redshift. Similar cases have been observed in the local Universe, for example, the Antennae galaxy which is a local merging system hosting six star-forming complexes whose SFR ranges from 0.2 to 1.4 M⊙ yr−1, significantly higher than other local star-forming regions. Three of those complexes show signatures of Wolf–Rayet stars, implying young ages of $\sim 5\, \rm Myr$ (Bastian et al. 2006). Similar properties are observed in the Sunburst 5.1 knot presented in detail in Vanzella et al. (2021a) and in some of our clumps shown in fig. D.1 of Vanzella et al. (2021b). Other scenarios, such as the fragmentation of gas-rich discs, are potentially responsible for the observed high SFR among high redshift clumpy structures (Noguchi 1999; Dekel et al. 2009). During this process, cold gas cools becoming unstable and the galactic disc starts to fragments and forms clumpy structures. Further on, such newly formed structures lead to the increased SFR as discussed in i.e. Immeli et al. (2004).

2019AandA...627A.172R__Rozitis_&_Green_(2013)_Instance_3

For comparisons with the light curve YORP constraints, the YORP effect acting on Cuyo could be predicted by computing the total recoil forces and torques from reflected and thermally emitted photons from the asteroid surface using the ATPM. These calculations were made for both a smooth and rough surface, and were averaged over both the asteroid rotation and elliptical orbit (see Rozitis & Green 2012, 2013, for methodology). As demonstrated in Rozitis & Green (2012), the inclusion of rough-surface thermal-infrared beaming effects in the YORP predictions tends to dampen the YORP rotational acceleration on average but can add uncertainties of up to several tens of per cent if the roughness was varied across the surface. Since the light curve inversion produced convex shape models only, then shadowing and self-heating effects inside global-scale concavities (see Rozitis & Green 2013) were not possible to model. However, a study of non-convex shape models for fast two to four hour rotators in Rozitis & Green (2013) indicated that such asteroids have rather minimal levels of global-scale concavities, and the ~ 2.7 h rotation period of Cuyo implies that its shape could be similar. Furthermore, the Tangential-YORP effect, that is, a predicted rotational acceleration caused by temperature asymmetries within exposed rocks and boulders on the surface of an asteroid (Golubov & Krugly 2012), was also not included in the ATPM predictions. However, the very low thermal inertia value measured for Cuyo implies the absence of rocks and boulders on its surface of the quantity and size that are necessary to induce a significant Tangential-YORP component. As Cuyo is likely to be an S-type rubble-pile asteroid, a bulk density equivalent to that measured for the S-type rubble-pile asteroid (25143) Itokawa (Abe et al. 2006) of 2 g cm−3 was assumed for the YORP computations. Using the thermo-physical properties derived earlier, the ATPM predicts YORP rotational acceleration of (−6.39 ± 0.96) × 10−10 rad day−2 for the nominal shape model. The uncertainty given here corresponds to the standard deviation of results when the degree of surface roughness israndomly varied across the surface of Cuyo (see Lowry et al. 2014, for details of the Monte Carlo methodology used). These values lie well within the light curve rotational acceleration constraints determined previously.

2017AandA...604A..53C__Bolatto_et_al._(2013)_Instance_1

The reasons for such a tight – and linear – correlation between \hbox{$L^{\prime}_{\rm CO(1{-}0)}$}LCO(1−0)′ and M∗ (or LK) observed consistently across different galaxy samples except in early type galaxies have been little explored in the literature. The interpretation favoured by Leroy et al. (2005) is that CO emission and stars are linked through the hydrostatic pressure in the disk, which depends mainly on the stellar surface density (Σ∗) and sets the rate at which Hi is converted into H2. An alternative explanation that we propose here goes back to the nature of the optically thick 12CO emission and to the approximately linear relation between 12CO luminosity and virial mass found for GMCs (see e.g. Scoville et al. (1987), Solomon et al. (1987), and Bolatto et al. (2013) for a more recent compilation). By extrapolating from the relation shown by Scoville et al. (1987), our observed \hbox{$L^{\prime}_{\rm CO(1{-}0)}-M_*$}LCO(1−0)′−M∗ correlation may be so tight and close to linear simply because the global 12CO luminosity is a very good tracer of the dynamical mass in star-forming galaxies, assuming that in this class of objects the bulk of the CO emission traces molecular gas clouds in virial motions (for example in a rotating disk). This explanation of course assumes that in most star-forming galaxies the stellar mass is also a good tracer of the dynamical mass. Following on from our hypothesis, a possible explanation for the break down of the \hbox{$L^{\prime}_{\rm CO(1{-}0)}-M_*$}LCO(1−0)′−M∗ relation in early types is that in these sources, even when CO is detected, the motions of the CO-emitting clouds are poor tracers of the total dynamical mass of the system. Interferometric CO observations of large samples of early type galaxies have indeed shown that the CO emission in these objects tends to be rather compact (on average extending over ~ 1 kpc) compared to the optical extent of the galaxy (Alatalo et al. 2013; Davis et al. 2013).

2020AandA...638A..44B__Jiménez-Serra_et_al._2010_Instance_1

Combining these different velocity signatures, the data show strong signatures of two gas components at different velocities (around 6 km s−1 apart) that converge to a common intermediate velocity at the location of the infrared dark cloud and active star-formingregion, similar to filament formation via gravitationally driven, converging gas flows (e.g., Gómez & Vázquez-Semadeni 2014). We interpret these signatures as indicators of converging gas streams that may trigger the star formation event at its center (e.g., Vázquez-Semadeni et al. 2006; Heitsch et al. 2008; Banerjee et al. 2009; Gómez & Vázquez-Semadeni 2014). Position-velocity diagrams based on simulations of cloud-cloud collisions sometimes show a characteristic pattern of lower-level emission between two main velocity components, a so-called bridging feature (e.g., Haworth et al. 2015, 2018). Similar signatures were also reported in observations (e.g., Jiménez-Serra et al. 2010; Henshaw et al. 2013; Dobashi et al. 2019; Fujita et al. 2019). The pv-diagrams of the G28.3 region presented here (Fig. 9) show different signatures in the sense that there is not a lower-intensity bridge between the two well-defined components, but that the two velocity components converge at the center of the cloud toward a central, high-intensity velocity component. However, the absences of a bridging feature does not necessarily rule out the formation of G28.3 in a cloud-cloud collision, as this feature is not always visible in simulations. For example, simulations by Bisbas et al. (2017) show that the low-intensity bridge feature may merge into a centrally peaked pv-diagram during the evolution of the cloud-cloud collisions, while the colliding-cloud simulations of Clark et al. (2019) yield only a single central velocity component in CO or [CI], with multiple components only becoming apparent when the [CII] emission from the cloud is considered. In addition, the multiple components and bridging feature may not be visible in cases where our line of sight is oriented at a large angle to the direction of motion of the clouds. We return to the interpretation in Sects. 4.4 and 4.5.

2021MNRAS.508..637S__Johnston_et_al._2019_Instance_1

We also present the correlation functions of centrals and satellites in Fig. 17. This is a worthwhile exercise for a variety of reasons, not least that there is information on the small-scale IA signal missed in the large-scale fits. While we cannot, at the present time, fit the IA signal on scales ∼1 h−1 Mpc, the qualitative comparison can be instructive. Given that there is some evidence that they behave differently, we consider blue and red satellites/centrals separately here. We also drop the full sample density tracer, and instead use one of the four (red/blue, satellite/central) subsamples. The motivation here is that, while on large scales, the density tracer is effectively just that: a probe of the large-scale matter distribution multiplied by a linear galaxy bias, on scales approaching the one halo regime this no longer holds. Fig. 17 shows these new data vectors. As shown we measure both wg + and w++, and recompute the covariance matrices with the appropriate densities. For reference, the dark red crosses also show the equivalent satellite/central red galaxy wg + correlations from KiDS×GAMA here (c.f. Johnston et al. 2019 fig. 7, red points/band). On large scales at least, our illustristng red sample is consistent with their measurements. There are a few interesting features here to note, however. First, we see a relatively strong red galaxy 1h contribution on scales 1 h−1 Mpc. Although the general trends match the real data, with ss and to a lesser extent cs exhibiting strong scale-dependent IAs in this regime, the magnitude is somewhat higher in our sample. This is particularly interesting, given that our sample characteristics are similar (〈L〉/L0 = 0.91 and 0.34 for our red and blue samples, respectively, compared with their ∼0.99 and 0.50). As discussed briefly in Section 6.2.1, we observe a persistent non-zero IA signal in blue galaxies on large scales; here we can see it is dominated by the cc correlation, with a smaller contribution from sc. Also notable is that, in line with some previous direct IA measurements (e.g. Singh et al. 2015), the large-scale satellite correlations do not appear to vanish on large scales. Focusing on the right-hand panels, the purple and pink points are consistently positive and non-zero. While small compared with the red central terms, and consistent with the dark red points from GAMA, there appears to be a detectable signal at the precision allowed by illustristng.

2021MNRAS.508..637S__Chang_et_al._2019_Instance_1

It is now well established that the weak lensing of distant galaxies by foreground mass provides a relatively clear window on to the large-scale structure of the Universe. This is true whether that foreground mass is in the form of discrete matter concentrations, as traced by galaxies (i.e. galaxy–galaxy lensing; Mandelbaum et al. 2013; Leauthaud et al. 2017; Joudaki et al. 2018; Prat et al. 2018; Blake et al. 2020), massive dark matter haloes (cluster lensing; Melchior et al. 2017; Dark Energy Survey Collaboration 2020), or the continuous large-scale matter distribution (cosmic shear; Heymans et al. 2013; Dark Energy Survey Collaboration 2016; Troxel et al. 2018; Chang et al. 2019; Hamana et al. 2020; Hildebrandt et al. 2020; Amon et al. 2021; Asgari et al. 2021; Secco, Samuroff et al. 2021). Though the measurement method and the exact form of the theory predictions differ slightly in the three cases, they are all fundamentally probes of the growth of structure at low redshift. Similarly cross-correlations between galaxy lensing and other observables can be powerful probes in their own right; recent examples include galaxy lensing × CMB lensing (Schaan et al. 2017), voids correlated with CMB lensing (Vielzeuf et al. 2021), and galaxy weak lensing crossed with gamma-ray emission (Ammazzalorso et al. 2020), each of which provide probes of dark matter with slightly different sensitivities. A measurement of cosmological weak lensing, however, is subject to a range of systematic effects; that is, observational effects that mimic a cosmological lensing signal, and so bias cosmological inference if one neglects them. Depending on the systematic in question, the most effective mitigation strategy may be quite different. In broad terms, however, the standard approach is to either (i) mitigate systematics where possible, either by applying a calibration to the data, or discarding the data points most strongly affected or (ii) marginalize over them with a parametric model. Often a combination of the two is appropriate, and the prior used in (ii) is informed by additional data or simulations, and detailed testing of the calibration step in (i).

2019MNRAS.488.5029H__Stacey_et_al._2010_Instance_2

For the first time, we detected [C ii] 158-μm emission from a GRB host galaxy at z > 2. This is the second detection of [C ii] 158-μm emission among known GRB host galaxies, following GRB 980425 (Michałowski et al. 2016). The [C ii] 158-μm fine structure line is the dominant cooling line of the cool interstellar medium, arising from photodissociation regions (PDR) on molecular cloud surfaces. It is one of the brightest emission lines from star-forming galaxies from FIR to metre wavelengths, almost unaffected by dust extinction. [C ii] 158-μm luminosity, L[C II], has been discussed as an indicator of SFR (e.g. Stacey et al. 2010). If L[C II] scales linearly with SFR, the ratio to FIR luminosity, L[C II]/LFIR, is expected to be constant, since LFIR is a linear function of SFR (e.g. Kennicutt 1998a). However, LC II/LFIR is not constant, but declines with increasing LFIR, known as the ‘[C ii] deficit’ (e.g. Luhman et al. 1998, 2003; Malhotra et al. 2001; Sargsyan et al. 2012; Díaz-Santos et al. 2013, 2017; Spilker et al. 2016). The [C ii] deficit persists when including high-z galaxies (e.g. Stacey et al. 2010; Wang et al. 2013; Rawle et al. 2014). In Fig. 5, we compare the [C ii] deficit in the GRB 080207 host and other star-forming galaxies. Two GRB hosts are shown by stars: GRB 080207 (orange star) and 980425 (blue star). The comparison sample is compiled from the literature up to z ∼ 3 (Malhotra et al. 2001; Cormier et al. 2010, 2014; Ivison et al. 2010; Stacey et al. 2010; Sargsyan et al. 2012; Farrah et al. 2013; Magdis et al. 2014; Brisbin et al. 2015; Gullberg et al. 2015; Schaerer et al. 2015). Active galactic nuclei are separated from star-forming galaxies based on either (i) the explicit description in the literature or (ii) EWPAH 6.2μm 0.1 (Sargsyan et al. 2012). As reported by previous studies (e.g. Maiolino et al. 2009; Stacey et al. 2010), high-z galaxies are located at a different place from local galaxies in the L[C II]/LFIR–LFIR plane.