Datasets:

adsabs

/

FOCAL

like 1

Follow

SAO/NASA Astrophysics Data System 13

2022MNRAS.509.3599T__Du_et_al._2015_Instance_2

Here we report the X-ray spectral and timing analysis of the joint XMM–Newton and NuSTAR observations of an IRAS 04416+1215, a nearby (z = 0.0889; Boller et al. 1992) hyper-Eddington AGN. The source is part of a XMM–Newton/NuSTAR campaign that aims to constrain the broad-band X-ray properties of eight super-Eddington AGN from the best sample of bona fide super-Eddington sources available, i.e. super-Eddington accreting massive black holes (SEAMBHs; Du et al. 2014, 2015; Wang et al. 2014) that contains exclusively objects with black hole masses estimated from reverberation mapping. In this campaign we are carrying out to study the broad-band X-ray properties of super-Eddington AGN, all the sources have new NuSTAR observations performed simultaneously with XMM–Newton or Swift-X-ray Telescope (XRT). IRAS 04416+1215 has bolometric luminosity $\log (L_{\rm bol}/\rm erg\, s^{-1})=47.55$, according to Castelló-Mor, Netzer & Kaspi (2016), and $\log (L_{\rm bol}/\rm erg\, s^{-1})=45.52$, according to Liu et al. (2021). The former estimate is computed using, for the SED fitting procedure, the Slone & Netzer (2012) code, including the comparison of the observed SED with various combinations of disc SEDs covering the range of mass, accretion rate, spins, and taking into account the correction for intrinsic reddening and host galaxy contribution. In the latter estimate, the SED fitting is done using the more semplicistic templates from Krawczyk et al. (2013). The dimensionless accretion rate (Du et al. 2014) and black hole mass of the source are $\log (\dot{\mathscr {M}})$ = $2.63^{+0.16}_{-0.67}$ and log (MBH/M⊙) = $6.78^{+0.31}_{-0.06}$ with the reverberation mapping technique (Du et al. 2015), respectively, where $\dot{\mathscr {M}}\equiv \dot{M}_{\bullet }c^2/L_{\rm Edd}$, $\dot{M}_{\bullet }$ is mass accretion rates, c is speed of light, and LEdd is the Eddington luminosity. The dimensionless accretion rate is estimated by $\dot{\mathscr {M}}=20.1\, \ell _{44}^{3/2}M_7^{-2}$ from the Shakura–Sunyaev disc model (Du et al. 2015), where ℓ44 is the 5100 Å luminosity in units of $10^{44}\, {\rm erg\, s^{-1}}$ and $M_7=M_{\bullet }/10^7\, \mathrm{M}_{\odot }$. This approximation is valid for $\dot{\mathscr {M}}\lesssim 10^3$. To compute the Eddington ratio we assumed the bolometric luminosity value from Castelló-Mor et al. (2016), which is a better and more trustable estimate of the bolometric luminosity of the source, obtaining λEdd ∼ 472. This value is in perfect agreement with the dimensionless accretion rate from Du et al. (2014). However even assuming the luminosity from Liu et al. (2021), with which the value of the accretion rate would be λEdd ∼ 4.40, the source would remain a super-Eddington accreting AGN. IRAS 04416+1215 turned out to be the most peculiar of our sample, it is classified as NLS1 galaxy, showing narrow Hβ line [full width at half-maximum (FWHM) = $1670 \, \rm km \, \rm s^{-1}$; Moran, Halpern & Helfand 1996] and very broad [O iii] (FWHM = $1150 \, \rm km \, \rm s^{-1}$; Véron-Cetty, Véron & Gonçalves 2001) lines, which is typically found in sources accreting at such high Eddington accretion rates (Greene & Ho 2005; Ho 2009). The source shows a photon index in the Roentgen Satellite (ROSAT) (0.1–2.4 keV) energy band, of Γ = 2.96 ± 0.50 (Boller et al. 1992) and of $\Gamma =2.46^{+0.27}_{-0.26}$ for the rest-frame >2 keV spectrum, according to Liu et al. (2021).

2022ApJ...935..127H__Pan_et_al._2021_Instance_1

Here we take the gravitational waveform of the nonrotating model with an observer viewed from the equatorial direction as an example for our HHT analysis. The gravitational waveforms of other models with different parameters are shown in the appendix (Figures B10–B20). Figure 6 (a) shows the simulated strains, followed by the wavelet spectra (b)–(c) and the stacked HHT spectra (d)–(e). The waveform starts from the core collapse and reaches the core bounce at t = 0 s. As this model is nonrotating and the proto-neutron star convection happens after the core bounce, the gravitational strain shows a loud bounce signal at time zero, and then it is followed by a low-frequency peak PNS oscillation, which could be caused by the g-mode oscillation although the nature remains controversial (Müller et al. 2013; Pan et al. 2021). Multiple low-frequency oscillation signals dominate the first ∼0.15 s, and they can be well resolved with the HHT. In comparison, the limitation of the wavelet spectrum prevents us to identify them. As the frequency of the oscillation increases with time, another high-frequency mode oscillation signal appears with similar strength as the peak PNS mode. The low-frequency PNS oscillation vanishes at t ∼ 0.5 s. Then, interestingly, it followed by the quadruple radiation of the standing accretion shock instability (SASI Blondin et al. 2003) signal, which was suggested to be seen in the GW data (Kuroda et al. 2016; Pan et al. 2021). The detailed waveform of the SASI signal is model dependent, but its time-frequency property has been shown in a few analysis either with the spectrogram or the HHT (see, e.g., Andresen et al. 2017; Mezzacappa et al. 2020; Takeda et al. 2021). In our analysis, the SASI signal can be seen on both the wavelet and the HHT spectra. The wavelet and the HHT results for other simulated CCSN events are shown in the Appendix B. The Hilbert spectra of all the data sets show much better resolution than the wavelet spectra.

2018MNRAS.475.3613S__Stabenau,_Connolly_&_Jain_2008_Instance_1

With the availability of such a wide range of photo-z codes and methods, comparisons of various implementations have been performed (Hildebrandt et al. 2010; Abdalla et al. 2011; Sanchez et al. 2014). No obvious best photo-z code was named since each code displays different strengths depending on the metrics used. The focus of recent photo-z analyses has turned to improving error estimation (Oyaizu et al. 2008; Hoyle et al. 2015; Wittman, Bhaskar & Tobin 2016), the use of new statistical techniques (Lima et al. 2008; Zitlau et al. 2016), improving existing algorithms (Cavuoti et al. 2015; Sadeh et al. 2016), and the addition of extra input information to get more precise and accurate photo-z’s. With regards to the inclusion of extra information, a recent example in template methods includes using surface brightness as a prior in spectral energy distribution templates (Kurtz et al. 2007; Stabenau, Connolly & Jain 2008), motivated by the knowledge of surface brightness dimming (1 + z)4. In empirical methods this application is more straightforward, since algorithms are constructed such that it is not difficult to add extra input parameters. For example, Collister & Lahav (2004) and Wadadekar (2005) demonstrated that by including the 50 and 90 per cent Petrosian flux radii (RP50, RP90), the photometric redshift root-mean-square errors improve by 3 and 15 per cent respectively for the SDSS main galaxy sample. Tagliaferri et al. (2003) used Petrosian fluxes and radii in their work on galaxies from the SDSS early data release and calculated robust errors decreasing as much as 24 per cent. Meanwhile, Vince & Csabai (2007) included the concentration of galaxy light profiles in their study and reported that the root-mean-square error of photo-z’s on SDSS galaxies improved by 3 per cent. Wray & Gunn (2008) included surface brightness and the Sérsic index, and found improvements in variance when compared to other template fitting methods applied to the SDSS main galaxy sample previously.

2020ApJ...904...20S__Woods_et_al._2008_Instance_1

Comparison of the DAXSS two temperature (2T) model fits to DEM estimates from other instruments could help validate the DAXSS simple modeling approach. Moore et al. (2018) shows that the X123 response is primarily over the temperature range of 1 MK to 10 MK, so this comparison needs to be done over a similar range, such as is accessible with using DEMs derived from extreme ultraviolet (EUV) emissions. One of the comparisons is with the DEM derived with SDO (Pesnell et al. 2012) EVE (Hock et al. 2012; Woods et al. 2012) solar EUV spectral irradiance data in the 6–37 nm range. The derivation of EVE-based DEM estimates is being developed for improving the X-ray ultraviolet Photometer System (XPS) data processing for the Solar Radiation and Climate Experiment (SORCE) (Woods et al. 2005a) and Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics (Woods et al. 2005b) missions. The primary DEMs for this analysis is the quiet Sun (QS) and active region (AR) DEMs as needed for estimating the daily variations of the solar XUV spectral irradiance for the XPS Level 4 product (Woods et al. 2008). The Fe viii to Fe xvi lines in the SDO EVE spectra were initially used to estimate the DEMs for the reference QS and AR spectra derived with EVE data between 2010 and 2013 using the technique described by Schonfeld et al. (2017). It was then found that fitting with DEM Gaussian profiles with logarithmic temperature (K, log(T)) peaks every 0.2 and with Gaussian width of 0.42 in log(T) (FWHM of 1.0) provided more robust solutions for the DEM (a similar technique was described by Warren et al. 2013). Furthermore, fitting just specific Fe lines was providing low irradiance estimates in the 6–15 nm range. Better spectral model values for this range was found when fitting the EVE spectra over the ranges of 10–14 nm and 26–30 nm. The DEM estimate using just the rocket EVE spectral data flown with DAXSS on 2018 June 18 and the DEM based on combining the QS DEM and the AR DEM with an AR scaling factor of 0.00806 are shown in Figure 12. This scaling factor for the AR EM was determined as the best fit for the DAXSS spectral irradiance. The conversion of the EVE-based DEM ( ) to EM (cm−3) for comparison to DAXSS 2T model solution is the multiplication by the solar hemisphere area ( ) and by the temperature bin size (0.23 * temperature in K).

2017MNRAS.468.4992P__Lister_et_al._2016_Instance_1

The intrinsic jet opening angles can be calculated as tan (αint/2) = tan (αapp/2) sin θ, where θ is the viewing angle to the jet axis. The latter, as well as the bulk Lorentz factor Γ, can be derived from apparent jet speed and Doppler factor using the following relations: \begin{eqnarray*} \theta = \arctan \frac{2\beta _{\rm app}}{\beta ^2_{\rm app}+\delta _{\rm var}^2-1}\,, \quad \Gamma = \frac{\beta _{\rm app}^2+\delta _{\rm var}^2+1}{2\delta _{\rm var}}\,. \end{eqnarray*} For βapp and δvar we used the fastest measured radial, non-accelerating apparent jet speed from the MOJAVE kinematic analysis (Lister et al. 2016) and the variability Doppler factor from the Metsähovi AGN monitoring programme (Hovatta et al. 2009), respectively. The corresponding overlap of the programmes comprises 55 sources, which are all members of the MOJAVE-1 sample. Variability Doppler-factors for 10 more MOJAVE-1 sources were measured within the F-GAMMA programme (Liodakis et al. 2017). The intrinsic opening angles calculated for the 65 sources range from 0$_{.}^{\circ}$1 to 9$_{.}^{\circ}$4, with a median of 1$_{.}^{\circ}$3, reflecting a very high degree of jet collimation. The intrinsic opening angles show an inverse dependence on Lorentz factor (Fig. 13), as predicted by hydrodynamical (Blandford & Königl 1979) and magnetic acceleration models (Komissarov et al. 2007) of relativistic jets. The median value of the product3 ρ = αintΓ is 0.35 rad, close to earlier estimates derived both from observations (Jorstad et al. 2005; Pushkarev et al. 2009) and from a statistical model approach (Clausen-Brown et al. 2013). The variability Doppler factors derived from variability can be underestimated due to a limited cadence of the observations. In this case, the intrinsic opening angle estimates would be smaller, while Lorentz factors would be higher if $\delta >(\beta _{\rm app}^2+1)^{1/2}$ and smaller otherwise, implying that if the variability Doppler factors are essentially underestimated, the majority of points in Fig. 13 would move downward and to the right.

2021MNRAS.501...50S__Gupta_et_al._2019_Instance_1

There have been rather strong claims of AGN QPOs in different bands of the electromagnetic spectrum, ranging from minutes through days through months and years (e.g. Gierliński et al. 2008; Lachowicz et al. 2009; Gupta, Srivastava & Wiita 2009; Gupta et al. 2018, 2019; King et al. 2013; Gupta 2014, 2018; Ackermann et al. 2015; Pan et al. 2016; Zhou et al. 2018; Bhatta 2019; and references therein). However, many of the claimed QPOs, particularly those made earlier, were marginal detections (Gupta 2014), lasting only a few cycles, and the originally quoted statistical significances are probably overestimates (Gupta 2014; Covino, Sandrinelli & Treves 2019). Among the better recent claims of QPOs in the gamma-ray band are of ∼34.5 d in the blazar PKS 2247–131 (Zhou et al. 2018) and of ∼71 d in the blazar B2 1520+31 (Gupta et al. 2019) found as part of a continuing analysis of blazar Fermi–LAT observations. A recent claim of a ∼44 d optical band QPO in the narrow-line Seyfert 1 galaxy KIC 9650712 from densely sampled Kepler data has been made by Smith et al. (2018); it was supported by an independent analysis of the same data, indicating a QPO contribution at 52 ± 2 d (Phillipson et al. 2020). Some possibly related QPOs of a few hundred days in two widely separated bands have been reported (Sandrinelli et al. 2016a; Sandrinelli, Covino & Treves 2016b; Sandrinelli et al. 2017). However, an analysis of the Fermi–LAT and aperture photometry light curves by Covino et al. (2019) argued that some multiwaveband QPOs, along with many earlier claims of gamma-ray QPOs, are not significant. Among the gamma-ray QPOs with month-like periods, none showed simultaneous oscillations in a different wavebands. Evidence for related QPOs in multiple wavebands was observed in PG 1553+113, where a QPO was detected in the 0.1–300 GeV and the optical waveband (Ackermann et al. 2015). The observed QPO had a dominant period of ∼754 d and the source showed strong inter-waveband cross-correlations.

2015MNRAS.450..943T__Patterson_1984_Instance_1

In order to recover the intrinsic SED, we employ the values for interstellar extinction E(B − V) from Schlafly & Finkbeiner (2011), collected on NASA's Infrared Science Archive (IRSA) web pages, to deredden the spectra. We use the relations derived by Cardelli, Clayton & Mathis (1989) as implemented in iraf, and a standard value for the ratio of the total to the selective extinction R(V) = A(V)/E(B − V) = 3.1. The corresponding values are available as the average extinction within a 2 × 2 deg2 field. This represents a severe limitation as it does not take into account potential small-scale variations in the dust structure, nor the actual amount of absorbing material in the line of sight to the target. Since CVs are intrinsically rather faint (typically, MV > 4 mag; e.g. Patterson 1984), in general the correction for the interstellar extinction will represent an upper limit. An additional uncertainty regarding the intrinsic SED is introduced by obtaining the flux calibration on the basis of a single standard star that was observed on a different night than the targets. Furthermore, the observations were not conducted at a parallactic angle and thus might be affected by chromatic slit loss due to differential refraction, since Gemini-South is not equipped with an atmospheric dispersion corrector. As a measure for the SED, we derive the exponent α of a power law F ∝ λ−α that was fitted to the spectra, restricting the continuum to wavelengths 5000–7000 Å and masking strong emission and absorption lines. During that analysis we found that most of the post-novae required a two-component fit while previously this represented an exception (Paper I, Paper IV). Furthermore, in every case the two slopes differed in the same direction, with the red part of the spectrum requiring a steeper slope than the blue part, and the points that separated the two slopes were found to be within 5850–5900 Å which marks the region that is affected by the gap between two CCDs (see Section 2). All this suggests a systematic effect in the definition of the instrumental response function, and potentially a difference in the spectral efficiency of these two CCDs. We have thus further divided the fitting range into a blue (5000 λ 5870 Å) and a red (5870 λ 7000 Å) part that are fitted individually. The consequences for the interpretation of the SEDs are further discussed in Section 4.

2021ApJ...922...85T__Porquet_et_al._2004_Instance_1

When the primary X-ray continuum is scattered by distant matter at several thousands of gravitational radii, the line is narrow (FWHM 10,000 km s−1) and is unlikely to carry any information about the conditions in the accretion disk and the strong-gravity regime near the SMBH. Narrow lines have been detected in the great majority of AGNs with luminosities L X,2.0−10.0 keV 1045 erg s−1. The mean FWHM reported from Chandra High Energy Transmission Grating (HETG) spectra is ∼2000 km s−1 (Yaqoob & Padmanabhan 2004; Shu et al. 2010, 2011; see also Nandra 2006). Low equivalent width (EW) values from Suzaku observations (tens of eV; Fukazawa et al. 2011a) have also been interpreted as evidence of narrow lines originating in distant material (Ricci et al. 2014). In contrast, when the primary X-ray continuum is scattered close to the SMBH, Doppler and general relativistic effects combined may give rise to a significantly broader line (FWHM tens of thousands km s−1), reported in at least ∼36% of AGNs (de la Calle Pérez et al. 2010; see also, e.g., Porquet et al. 2004; Jiménez-Bailón et al. 2005; Guainazzi et al. 2006; Nandra et al.2007; Brenneman & Reynolds 2009; Patrick et al. 2012; Liu & Li 2015; Mantovani et al. 2016; Baronchelli et al. 2018). In this case, contributions to the line’s width become increasingly stronger as the primary continuum is scattered closer, and up to, the innermost stable circular orbit (ISCO), where the accretion disk’s inner edge is located. Since the ISCO location depends directly on black hole spin, the latter leaves an imprint on the broad line’s profile and can in principle be measured by means of relativistic modeling of the line via the X-ray reflection method of spin determination. As a result, there is a significant number of SMBH spin measurements (e.g., Brenneman 2013, and references therein). SMBH spin constraints have very significant implications for understanding both SMBHs and the way they affect their environment. Apart from potentially constituting a Kerr-metric-based test of general relativity in the strong-field regime, spin measurements can inform on jet-driving mechanisms, e.g., via extraction of SMBH rotational energy (Blandford & Znajek 1977), which in turn critically affect galactic environments and evolution.

2021MNRAS.504.6198M__McCoy_et_al._2017_Instance_1

Here, we present the GMC properties within the molecular disc of the closest giant elliptical galaxy, NGC 5128, which is the host of the radio-source Centaurus A (hereafter Cen A). Cen A is at a distance of only D ≃ 3.8 Mpc (Harris, Rejkuba & Harris 2010, 1 arcsec = 18 pc) and it is therefore by far the most adequate target in the class of giant elliptical galaxies as well as powerful radio galaxies for studies of their molecular gas with high resolution. Indeed, Cen A is a peculiar case of an elliptical galaxy whose gaseous component has been supplied a few 0.1 Gyr ago by the accretion of a HI rich galaxy (e.g. Struve et al. 2010). Along the dust lane of the elliptical galaxy, there is a molecular gas component of mass ∼109 M⊙ as probed by various molecular lines (e.g. Phillips et al. 1987; Eckart et al. 1990; Rydbeck et al. 1993; Liszt 2001; Espada et al. 2009; Espada 2013; McCoy et al. 2017), partially seen in the form of kpc scale spiral features (Espada et al. 2012). The dust lane is along the minor axis, different to other ellipticals where discs are usually along the major axis (Young 2002). The molecular gas is associated with other components of the ISM, such as ionized gas traced by the H α line (e.g. Nicholson, Bland-Hawthorn & Taylor 1992), near-infrared continuum (Quillen, Graham & Frogel 1993), submillimetre continuum (e.g. Hawarden et al. 1993; Leeuw et al. 2002), and mid-IR continuum emission (e.g. Mirabel et al. 1999; Quillen et al. 2006). In the inner hundreds of parsecs there is a circumnuclear disc (CND) of 400 pc total extent (∼24 arcsec) and a P.A. = 155°, perpendicular to the inner jet, at least as seen in projection (Espada et al. 2009). The total gas mass in this component has been estimated to be 9 × 10$^7\, \mathrm{M}_{\odot }$ (Israel et al. 2014, 2017). More detailed studies of the CND with higher resolutions of ∼5 pc in CO(3–2) and CO(6–5) have revealed the complexity of the molecular gas distribution and kinematics in that region, with multiple internal filaments and shocks (Espada et al. 2017).

2015ApJ...811L..32H__Hellinger_et_al._2013_Instance_1

During the second phase, protons are heated. For negligible heat fluxes, collisions, and fluctuations, one expects the double adiabatic behavior or CGL (Chew et al. 1956; Matteini et al. 2012): the parallel and perpendicular temperatures (with respect to the magnetic field) are expected to follow and respectively. Tp⊥ decreases slower than during the whole simulation; protons are heated in the perpendicular direction while in the parallel direction the heating lasts till about t ∼ 0.25te0, whereas afterward protons are cooled. The parallel and perpendicular heating rates could be estimated as (see Verscharen et al. 2015) 1 A more detailed analysis indicates that between t = 0.1te0 and t = 0.7te0 the parallel heating rate smoothly varies from about 0.2 Qe and whereas is about constant at ∼0.2 Qe; here, In total, protons are heated until t ∼ 0.7te0, and the heating reappears near the end of the simulation t ≳ 0.95te0. Note that the perpendicular heating rate is a nonnegligible fraction of that observed in the solar wind, where (Hellinger et al. 2013); however, the proton heating in 2D hybrid simulations is typically quite sensitive to the used electron equation of state (Parashar et al. 2014) and also to the resistivity and the number of particles per cell used (Franci et al. 2015a). The turbulent heating is, however, not sufficient to overcome the expansion-driven perpendicular cooling as in the solar wind (Matteini et al. 2007). During the third phase, t ≳ 0.7te0; there is an enhancement of the parallel cooling and perpendicular heating that cannot be ascribed to the effect of the turbulent activity. For a large parallel proton temperature anisotropy, a firehose instability is expected. The presence of such an instability is supported by the fact that the fluctuating magnetic field increases (with respect to the linear prediction), suggesting a generation of fluctuating magnetic energy at the expense of protons. To analyze the role of different processes in the system, we estimate their characteristic times (Matthaeus et al. 2014). The bottom panel of Figure 2 compares the turbulent nonlinear eddy turnover time at kdp = 1 (see Matthaeus et al. 2014; the expansion time te, and the linear time tl of the oblique firehose (Hellinger & Matsumoto 2000, 2001) estimated as where γm is the maximum growth rate calculated from the average plasma properties in the box assuming bi-Maxwellian proton velocity distribution functions (Hellinger et al. 2006). The expansion time te is much longer than tnl at kdp = 1 (as well as at the injection scales). The expanding system becomes theoretically unstable with respect to the oblique firehose around t ∼ 0.47te0 but clear signatures of a fast proton isotropization and of a generation of enhanced magnetic fluctuations appear later t ≳ 0.7te0. This is about the time when the linear time becomes comparable to the nonlinear time at ion scales. After that, slightly increases as a result of a saturation of the firehose instability, whereas at kdp = 1 is about constant (note that Ωp decreases as R−2). This may indicate that the instability has to be fast enough to compete with turbulence; however, the 2D system has strong geometrical constraints. Also the stability is governed by the local plasma properties. Figure 3 shows the evolution of the system in the plane During the evolution, a large spread of local values in the 2D space develops. Between t ≃ 0.1te0 and t ≃ 0.65te0, the average quantities evolve in time following This anticorrelation is qualitatively similar to in situ Helios observations between 0.3 and 1 AU (Matteini et al. 2007). During the third stage, when the strong parallel temperature anisotropy is reduced, both local and average values of and Ap appear to be bounded by the linear marginal stability conditions of the oblique firehose (Hellinger & Trávníček 2008), although relatively large theoretical growth rates are expected.

2021MNRAS.503.2776Y__Ajith_et_al._2007_Instance_2

In order to investigate the signal-to-noise ratio (SNR), ρ of NS–WD binaries for LISA-type space GW detectors, we calculate the averaged square SNR $\overline{\rho ^{2}}$ over the sky location, inclination, and polarization as (30)$$\begin{eqnarray*} \overline{\rho ^{2}} = \int _{f_{1}}^{f_{2}}\frac{4\cdot \frac{4}{5}fA^{2}(f)}{(P_{\rm n}(f)/R(f))} \rm d (\ln \it f), \end{eqnarray*}$$(Moore, Cole & Berry 2015; Robson, Cornish & Liu 2019), where f1 and f2 are the lower and upper limits of the integral, respectively. The factor 4 in the numerator of the integrand comes from the addition of strain noise in the detector arms and the two-way noise in each arm (Larson, Hiscock & Hellings 2000). We calculate the GW amplitudes A(f) of NS–WD binaries using the phenomenological (PhenomA) waveform model in the Fourier domain (Ajith et al. 2007; Robson et al. 2019). A(f) is expressed as (31)$$\begin{eqnarray*} A(f) = \sqrt{\frac{5}{24}}\frac{G^{5/6}\mathcal {M}^{5/6}}{\pi ^{2/3}c^{3/2}R_{\rm b}}f^{-7/6}\, {\rm Hz}^{-1},\,\,\,\it f\lt f_{\rm m}, \end{eqnarray*}$$ (32)$$\begin{eqnarray*} f_{\rm m} = \frac{0.2974\zeta ^{2}+0.04481\zeta +0.09556}{\pi (GM/c^{3})}\, {\rm Hz}, \end{eqnarray*}$$ (33)$$\begin{eqnarray*} \zeta = m_{1}m_{2}/M^{2}, \end{eqnarray*}$$where $\mathcal {M} \equiv m_1^{3/5} m_2^{3/5}/(m_1+m_2)^{1/5}$, and fm is the GW frequency at the point of merging. If f > fm, the index of the power-law relation between A(f) and f changes (Ajith et al. 2007) and is beyond the scope of this study. The power spectral density of total detector noise $P_{\rm n}=\frac{1}{L^{2}}\left[P_{\rm o}+2(1+\cos ^{2}(f/f_{\ast }))\frac{P_{\rm a}}{(2\pi f)^{4}}\right]$, where f* = c/(2πL), L = 2.5 × 109 m is the armlength of the detector, $P_{\rm o}=2.25\times 10^{-22} \,\rm m^{2}\left(1+(\frac{2\,mHz}{\it f})^{4}\right) \,\, \rm Hz^{-1}$ is the single-link optical metrology noise, and $P_{\rm a}=9.0\times 10^{-30} \,\rm (m\,s^{-2})^{2}\left(1+(\frac{0.4\,mHz}{\it f})^{2}\right)\left(1+(\frac{\it f}{8\,\rm mHz})^{4}\right) \,\,Hz^{-1}$ is the single test mass acceleration noise (LISA Science Study Team 2018; Robson et al. 2019). R(f) is the transfer function numerically calculated from Larson et al. (2000). The effective noise power spectral density can be defined as Sn(f) = Pn(f)/R(f). For Taiji and Tianqin, we use the sensitivity curve data in Ruan et al. (2020) and Wang et al. (2019), respectively.

2019MNRAS.484.2000D__Sandage_&_Fouts_1987_Instance_1

To understand the distribution of Li-rich giants among different stellar components of the Galaxy, we have computed membership probability, based on the recipe given in Reddy, Lambert & Allende Prieto (2006) and references therein, for each of the stars in the selected RGB sample for belonging to one of the three main components of the Galaxy, namely; thin disc, thick disc, and halo. For this, the heliocentric velocities (U, V, W) for each of the sample giant is calculated using the Gaia astrometry (positions, parallax, and proper motions) and radial velocities (RV) from the GALAH DR2. The heliocentric velocities are corrected for the solar motion using Uo = 10, Vo = 5.3, Wo = 7.2 (km s−1) from Dehnen & Binney (1998) to get velocities with respect to the local standard of rest (ULSR, VLSR, WLSR). Entire sample is shown in Toomre Diagram of rotational velocity (VLSR) and sum of quadrature of radial and vertical velocities ([$\rm {\it U}_{\rm LSR}^2 + {\it W}_{\rm LSR}^2]^{1/2}$) (Sandage & Fouts 1987). Li-rich giants are identified by coloured symbols as shown in Fig. 4. We used kinematic boundaries for thin disc ($|V_{\rm Total}|$ = $[U_{\rm LSR}^2 + V_{\rm LSR}^2 + W_{\rm LSR}^2]^{1/2}$ ≤ 80 km s−1), thick disc (80 |VTotal| ≤ 200 km s−1) and halo (|VTotal| > 200 km s−1) which are in accordance with the results in Reddy et al. (2006). Further to quantify the number of giants belonging to different components of the Galaxy, we used probability of 70${{\ \rm per\ cent}}$ or more for any star being considered as a member of particular component. We found 223 Li-rich thin disc stars, which is about 0.8${{\ \rm per\ cent}}$ of total thin disc RGB stars with $P_{\rm thin} \ge 70 {{\ \rm per\,cent}}$ and 69 Li-rich thick disc stars consisting of 0.5${{\ \rm per\ cent}}$ of total thick disc RGB stars with $P_{\rm thick} \ge 70{{\ \rm per\,cent}}$. We have found just three Li-rich giants among 1442 halo giants with $P_{\rm halo} \ge 70{{\ \rm per\,cent}}$. This shows that Li-rich giants are rare ($\lt 1{{\ \rm per\,cent}}$) across stellar components but are relatively more prevalent among metal-rich thin disc component compared to thick disc and very metal poor halo.

2022AandA...659A.180G__Giannattasio_et_al._2013_Instance_1

In the last few decades, the dynamic properties of the quiet Sun have been thoroughly investigated using a range of substantially different techniques, allowing us to elaborate a consistent picture of the photospheric dynamics by approaching the problem from different points of view. Particularly interesting and promising are the studies involving the tracking of small-scale magnetic fields in the quiet photosphere. Such investigations reveal features that still cannot be captured by theoretical models and/or simulations because of the complexity of the system and the simultaneous coupling of a wide range of spatial and temporal scales. These studies are based on the hypothesis that magnetic fields are passively transported by the plasma flow and provide a characterisation of advection and diffusion processes in the quiet photosphere from granular to supergranular scales (see, e.g. Wang 1988; Berger et al. 1998; Cadavid et al. 1998, 1999; Hagenaar et al. 1999; Lawrence et al. 2001; Sánchez Almeida et al. 2010; Abramenko et al. 2011; Manso Sainz et al. 2011; Lepreti et al. 2012; Giannattasio et al. 2013, 2014a,b; Giannattasio et al. 2019; Keys et al. 2014; Caroli et al. 2015; Del Moro et al. 2015; Yang et al. 2015a,b; Roudier et al. 2016; Abramenko 2017; Kutsenko et al. 2018; Agrawal et al. 2018; Giannattasio & Consolini 2021). These studies reveal an anomalous scaling of magnetic field transport with a superdiffusive character consistent with a Levy walk inside supergranules and a more Brownian-like motion in their boundary. This has provided constraints on the magnetic flux emergence and evolution that models have to consider in order to fully explain the dynamics governing these environments. At the same time, the scaling laws affecting magnetic fields in the quiet Sun are crucial to understanding how such fields vary with scale size, despite the small scales at which dissipation occurs still being inaccessible with the currently available observations (see, e.g. Lawrence et al. 1994; Stenflo 2012, and references therein). For example, in the milestone work by Stenflo (2012), the spectrum of magnetic flux density in the quiet Sun was found to be consistent with a Kolmogorov power-law scaling. The scale at which scale invariance is broken lies below the current resolution limit. This latter author argued that the collapse of magnetic fields in Kilogauss flux tubes injects energy that is expected to cascade down because of the flux decay occurring via interchange instability, and to fragment into weaker ‘hidden’ fields at smaller scales (down to ∼10 km). As far as we know, no other studies have focused on the scaling properties characterising the magnetic fields in a quiet Sun region within a range of spatial and temporal scales from (sub)granular to supergranular in the time domain. In this work, for the first time we apply the structure function analysis typical of complex systems (Frisch 1995) to fill this gap. This approach complements the studies based on feature tracking mentioned above. The main difference is that, while in those works statistical properties of the photospheric plasma flows are investigated via the transport of small-scale magnetic fields in a frozen-in condition, here we directly study the magnetic field variations emerging from magnetogram time-series. The paper is organised as follows. Section 2 describes the data set used and the analysis techniques applied. Section 3 describes the obtained results, while Sect. 4 is devoted to their discussion in the light of current literature. Finally, in Sect. 5, we present our conclusions and present future perspectives.

2022ApJ...930..125L__Zimbardo_et_al._2017_Instance_1

To this we can add that Zimbardo and coworkers reported in a series of papers that spacecraft observations of SEP intensity-time profiles ahead of interplanetary shocks yielded power laws instead of the exponential profiles predicted by standard DSA. They interpreted this as a sign of superdiffusive spatial SEP transport in the shock vicinity resulting in superdiffusive shock acceleration. They predicted the superdiffusive shock acceleration to be more efficient, producing a harder accelerated power-law spectrum for SEPs, than standard DSA (Zimbardo et al. 2006; Perri & Zimbardo 2007, 2008, 2009; Zimbardo & Perri 2013; Zimbardo et al. 2017, 2020; Zimbardo & Perri 2020). Zimbardo & Perri (2020) explained such superdiffusive transport as a product of subdiffusion in pitch-angle space arising from the observation that Alfvén-wave pitch-angle scattering times in the solar wind have a power-law distribution instead of a single value. Another possibility is that there is a strong presence of SMFRs in the vicinity of heliospheric shocks so that particle trapping effects could result in anomalous diffusive transport and shock acceleration. Evidence is increasing that, in a space plasma with a significant guide/background magnetic field like the solar wind, SMFRs naturally form as part of a self-generated, quasi-two-dimensional (quasi-2D) MHD turbulence component that might dominate other MHD wave turbulence modes. This is supported by observations in the slow solar wind near 1 au (e.g., Matthaeus et al. 1990; Bieber et al. 1996; Greco et al. 2009; Zheng & Hu 2018), MHD simulations (e.g., Shebalin et al. 1983; Dmitruk et al. 2004), and nearly incompressible MHD turbulence transport theory (e.g., Zank & Matthaeus 1992, 1993; Zank et al. 2017, 2018, 2020). It is plausible that SMFRs can have a strong presence ahead of interplanetary shocks. Furthermore, there is evidence that the occurrence of SMFRs peaks at large-scale current sheets in the solar wind where magnetic reconnection produces additional SMFRs that can trap energetic particles (Khabarova et al. 2015, 2016; Hu et al. 2018). Many of these current sheets occur behind traveling shocks (Khabarova & Zank 2017). Recently, a theoretical investigation was launched to determine the transmission of quasi-2D SMFRs through a perpendicular shock (Zank et al. 2021). The results show a strong enhancement in the magnetic energy density of the SMFR magnetic island component behind the shock, thus confirming a strong presence of SMFRs downstream of the shock as well.

2018ApJ...854...26L___2015a_Instance_4

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

2015MNRAS.454.1117S___2005_Instance_1

One of the first models of MRI-driven accretion in protoplanetary discs was constructed by Gammie (1996), who proposed that Ohmic resistivity (due to weak coupling of charged species to the magnetic field) quenches the MRI in a ‘dead’ mid-plane region surrounded by ‘active layers’, which are ionized by cosmic rays. This layered-accretion scenario has been refined as additional non-thermal ionization sources (X-rays, UV radiation) and non-ideal magnetohydrodynamic (MHD) effects (ambipolar diffusion and the Hall effect) have been included (e.g. Igea & Glassgold 1999; Sano et al. 2000; Fromang, Terquem & Balbus 2002; Salmeron & Wardle 2003, 2005, 2008; Ilgner & Nelson 2006; Bai & Goodman 2009; Bai & Stone 2011; Wardle & Salmeron 2012). Several theoretical studies, spanning both analytical (e.g. Blaes & Balbus 1994; Desch 2004; Kunz & Balbus 2004) and numerical work (e.g. Hawley & Stone 1998; Bai & Stone 2011, 2013b; Simon et al. 2013a,b), have shown that ambipolar diffusion plays a major role in determining the level of turbulence in protoplanetary discs. In the outer regions (radii R ≳ 30 au), ambipolar diffusion acts to reduce the strength of MRI-driven turbulence close to the disc mid-plane; turbulence is stronger above this ‘ambipolar damping zone’ in a thin layer of gas strongly ionized by FUV photons (Perez-Becker & Chiang 2011; Simon et al. 2013a,b). To produce accretion rates consistent with observations, a large-scale magnetic field threading the disc perpendicular to the disc plane is required (Simon et al. 2013a,b). This net field enhances turbulence, despite the damping effect of ambipolar diffusion, and allows additional angular-momentum loss via a magnetic wind (akin to the Blandford & Payne 1982 mechanism). In the inner disc, both local (Bai & Stone 2013b) and global (Gressel et al. 2015) simulations have shown that ambipolar diffusion can quench the MRI in the purported active layers of Gammie's (1996) model. In those cases, a large-scale magnetic field is again necessary to drive accretion at the observationally inferred rates, this time solely via a magnetic wind in a manner akin to previous models of wind-driven accretion in protoplanetary discs (e.g. Wardle & Koenigl 1993; Shu et al. 1994; Salmeron, Königl & Wardle 2007).

2020ApJ...896...59A__Henry_1989_Instance_1

PNs are expanding shells of the luminous gas expelled by dying stars of low and intermediate masses (LIMS). They stem from objects that have lifetimes up to gigayears. The ionized gas surrounding the central star shows emission lines of highly ionized species from which the abundances can be derived. The neon abundance can be compared and related to the results from stellar abundance analysis, as neon (and oxygen) originates from primary nucleosynthesis in massive stars (≥10 M⊙) and is therefore nearly independent of the evolution of LIMS, the progenitor stars of PNs (Henry 1989; Henry et al. 2004). For comparison, we collected several studies, where the neon abundances were obtained in PNs. We would not say that the abundances from PNs are very accurate, because the differences between abundances from collisionally excited lines and optical recombination lines can be much higher in PNs compared to those of H ii regions. For example, according to Wang & Liu (2007), in four Galactic disk PNs (He 2–118, H 1-35, NGC 6567, and M1-61), the mean neon abundance calculated from collisionally excited lines is 7.78 ± 0.23, while it is 8.72 ± 0.60 from optical recombination lines. Figure 9 presents neon abundances from different sources, including five studies focused on PNs. In those studies, where it was mentioned, we adopted neon abundances obtained from collisionally excited lines, e.g., Tsamis et al. (2003) and Wang & Liu (2007). The mean value of log ϵNe = 7.84 ± 0.24 was calculated on the basis of neon abundances in six Galactic disk PNs (Wang & Liu 2007). The mean value of the neon abundance log ϵNe = 7.99 ± 0.22 dex was calculated from six PNs (Hu 1-2, IC 418, NGC 40, NGC 2440, NGC 6543, NGC 7662) with Galactocentric distances from 7.9 kpc to 8.9 kpc from the summary of Pottasch & Bernard-Salas (2006). The value of log ϵNe = 7.76 ± 0.24 dex was derived from 16 Galactic PNs located at Galactocentric distances from 8.0 to 8.9 kpc in Stanghellini et al. (2006). The log ϵNe = 8.02 ± 0.25 dex was obtained from Marigo et al. (2003) from three PNs (NGC 6543, NGC 7027, NGC 7662), which are located at distances no more than 1.0 kpc. We adopted the mean value log ϵNe = 8.15 ± 0.21 dex, which was calculated from 16 Galactic PNs from Tsamis et al. (2003).

2020MNRAS.497..829F__Diaz-Miller,_Franco_&_Shore_1998_Instance_1

In order to consider both the photoionization and radiation-pressure effects from the accreting protostar, we solve the frequency-dependent radiative transfer for stellar irradiation by the following method. We inject photons from the sink cell to the computational domain at the rate (10)$$\begin{eqnarray*} L_{\rm *}^{\rm tot} = L_* + L_{\rm acc}, \end{eqnarray*}$$where L* is the stellar luminosity and $L_{\rm acc} \equiv G M_* \dot{M}_* / R_*$ is the accretion luminosity. The radiation spectrum is assumed to be thermal blackbody $L_{*, \nu }^{\rm tot} \propto B_\nu (T_{\rm eff})$, where Teff is the effective temperature defined as (11)$$\begin{eqnarray*} T_{\rm eff} = \left(\frac{L_*^{\rm tot}}{4 \pi \sigma R_*^2} \right)^{1/4} . \end{eqnarray*}$$Assumption of the thermal blackbody radiation could overestimate the emissivity of ionizing photons at Z ∼ Z⊙ owing to the line-blanketing effect, especially for stars with M* ∼ 10 M⊙ (e.g. Diaz-Miller, Franco & Shore 1998). This approximation should be valid in our cases since we mainly consider the feedback from more massive stars exceeding 100 M⊙. As discussed in Section 2.2.3, the effective temperature is higher for lower metallicity at a given mass: at M* = 100 M⊙, for instance, Teff ≃ 105 K for Z = 0, Teff ≃ 7 × 104 K for Z = 10−2 Z⊙, and Teff ≃ 5 × 104 K for Z = Z⊙. We use a hybrid method where the direct light emitted from the central star and diffuse light re-emitted from the accretion envelope are separately solved (e.g. Kuiper et al. 2010a, b). We solve the transfer of the stellar direct component by means of the ray-tracing method, (12)$$\begin{eqnarray*} F_{\nu }(r) = \frac{L_{*,\nu }^{\rm tot}}{4 \pi r^2} \mathrm{ e}^{-\tau _{\nu }}, \end{eqnarray*}$$where Fν(r) is the flux at the radial position r and τν is the optical depth (13)$$\begin{eqnarray*} \tau _{\nu } = \int _{r_{\rm in}}^{r} [ n_{\rm H\,{\small I}} \sigma _{\rm H\,{\small I}}(\nu) + \rho \kappa _{\rm \mathrm{ d}} (\nu)] \mathrm{ d}r, \end{eqnarray*}$$where σH i(ν) is the H i photoionization cross-section (e.g. Osterbrock 1989), κd(ν) the dust opacity given by Laor & Draine (1993), and rin the sink radius. We assume that the dust-to-gas mass ratio decreases with decreasing metallicities, linearly scaling with Z. The dust opacity is determined by the scaled dust-to-gas mass ratio. We also assume that photons freely travel without attenuation for R* r rin. We set 200 logarithmically spaced wavelength bins between 0.03 cm and 1.5 nm, achieving the higher resolution for the shorter wavelengths. The hydrogen photoionization rate is calculated by integrating the contributions by photons below the Lyman limit, i.e. 91.2 nm (Appendix A2.3). We do not include the diffuse ionizing photons, as their contribution to the disc photoevaporation is negligible compared with the stellar direct component in the massive star formation (e.g. Hosokawa et al. 2011; Tanaka, Nakamoto & Omukai 2013). As in Hosokawa et al. (2016), we do not incorporate helium ionization for simplicity. We assume that helium is in the atomic state everywhere including H ii regions. We consider the transfer of He ionizing photons above 54.4 eV emitted from the protostar, but they are consumed to photoionize hydrogen in our simulations. Regarding the photodissociation of hydrogen molecules, we do not use Fν(r) because our wavelength grids are too sparse to resolve the Lyman–Werner bands. We instead consider another monochromatic direct component representing FUV (11.2 eV ≤ hν ≤ 13.6 eV) photons (14)$$\begin{eqnarray*} F_{\rm FUV} (r) = \frac{L_{\rm FUV}}{4 \pi r^2} \exp (- \tau _{\rm d, FUV}), \end{eqnarray*}$$where LFUV is the luminosity in the FUV wavelength range, and τd,FUV is the dust opacity represented by the value at λ = 100 nm. The photodissociation rate of hydrogen molecules is estimated with the flux FFUV, including the $\rm H_2$ self-shielding. The details of the calculation are shown in Appendix A2.3

2021AandA...651A..71L__Samland_et_al._2017_Instance_1

Both SHINE and GPIES surveys recently discovered three new exoplanets (Macintosh et al. 2015; Chauvin et al. 2017b; Keppler et al. 2018) and a few additional higher-mass brown dwarfs (Konopacky et al. 2016; Cheetham et al. 2018). Smaller surveys using SPHERE and GPI also discovered several substellar companions (Milli et al. 2017a; Wagner et al. 2020; Bohn et al. 2020). These surveys offer unprecedented detection, astrometric, and spectrophotometric capabilities that allow us to characterize fainter and closer giant planets, such as the recent discovery of 51 Eri b (2 MJup at 14 au, T5-type, of an age of 20 Myr; Macintosh et al. 2015; Samland et al. 2017), HIP 65426 b, a young, warm, and dusty L5-L7 massive Jovian planet located at about 92 au from its host star (Chauvin et al. 2017b), and the young solar analogue PDS 70, which is now known to actually host two planets PDS 70 b discovered during the SHINE campaign (Keppler et al. 2018; Müller et al. 2018) and PDS 70 c by MUSE (Haffert et al. 2020). Such surveys also provide key spectral and orbital characterisation data for known exoplanets (e.g., De Rosa et al. 2016; Samland et al. 2017; Chauvin et al. 2018; Wang et al. 2018; Cheetham et al. 2019; Lagrange et al. 2019; Maire et al. 2019). Despite these new discoveries, SHINE and GPIES have yielded significantly fewer exoplanet detections than predicted, with their use of extrapolations of radial velocity planet populations to larger semi-major axes (e.g., Cumming et al. 2008). This results in our setting strong statistical constraints on the distribution of giant exoplanets at separations of >10 au from their stars, as well as sub-stellar companions to young stars (Nielsen et al. 2019; Vigan et al. 2021). As these systems are young (100 Myr), and thus closer to their epoch of formation than, for instance, radial-velocity planets (typically 1–10 Gyr), statistical analyses of large direct-imaging surveys can provide hints for the potential formation mechanism responsible for producing giant exoplanets on wide orbits.

2019ApJ...883...73C__Engelbrecht_&_Burger_2013a_Instance_1

Both Nel (2016) and Zhao et al. (2018) report that these parallel mean free paths are smaller during periods of high solar activity and larger during solar minima, with the opposite being true for the resulting perpendicular mean free paths. Zhao et al. (2018) also show that such solar cycle dependences would not be fully accounted for if only the solar cycle changes in the HMF were taken into consideration. These mean free paths also display complicated spatial dependences if turbulence quantities beyond 1 au, yielded by various turbulence transport models (see, e.g., Oughton et al. 2011; Usmanov et al. 2016; Wiengarten et al. 2016; Zank et al. 2017), taking into account observed latitudinal variations of turbulence quantities in the heliosphere (Forsyth et al. 1996; Bavassono et al. 2000a, 2000b; Erdös & Balogh 2005), were used as inputs for the turbulence quantities (e.g., Engelbrecht & Burger 2013a, 2015b; Chhiber et al. 2017; Moloto et al. 2018). Furthermore, mean free paths such as those discussed have, when used in conjunction with turbulence-reduced drift coefficients (see, e.g., Minnie et al. 2007b; Engelbrecht et al. 2017) in 3D stochastic modulation codes, led to computed galactic intensities in reasonable agreement with spacecraft observations at Earth (see, e.g., Engelbrecht & Burger 2013a, 2013b; Qin & Shen 2017) and even for several different solar minima (Moloto et al. 2018). The necessity of taking cosmic-ray drift effects into account, combined with the complicated spatial dependences and the fact that none of the diffusion coefficients described above display a P1 rigidity dependence, implies that a convection–diffusion or force-field approach would not be ideal to describe long-term modulation, and that the assumptions implicit to the effective diffusion coefficient used in these formulations are unrealistic. This latter point would call into question any conclusions drawn as to historic solar parameters from quantities such as the modulation potential.

2022MNRAS.516.3381J__Lindblom_&_Owen_2002_Instance_2

Studying the dynamical properties of rotating neutron stars is a domain which brings out various interesting features when one assumes a perfect fluid. It is known that the centrifugal force of a rotating star counters gravitational force and hence one can expect massive stars to be fast rotors, at least in the initial stages of the stellar evolution. As a result of rotation a star may experience damping due to unstable oscillations such as the r-modes. The r-modes are one of many pulsating modes that exist in neutron stars and are characterized by the Coriolis force acting as the restoring force (Andersson 1998). The r-modes are unstable to emission of gravitational radiation (GR) by the Chandrashekhar-Friedman-Schutz (CFS) mechanism (Chandrasekhar 1970; Friedman & Schutz 1978). It was shown in Andersson (1998) that the r-modes are unstable for all rotating perfect fluid stars irrespective of their frequency. Dissipative effects such as shear and bulk viscosities work towards suppressing GR driven instabilities and has been studied by various authors over the past few years (Lindblom, Owen & Morsink 1998; Jones 2001; Lindblom & Owen 2002; van Dalen & Dieperink 2004; Drago, Lavagno & Pagliara 2005; Nayyar & Owen 2006; Jaikumar, Rupak & Steiner 2008; Jha, Mishra & Sreekanth 2010; Ofengeim et al. 2019) under various considerations. If the GR time-scale is shorter than the damping time-scale due to such dissipative processes, then the r-mode will be unstable and a rapidly rotating neutron star could lose a significant fraction of its rotational energy through GR. At higher temperatures (T > 109 K), the dominant dissipation is due to bulk viscosity, which arises due to density and pressure perturbations, a consequence of the star being driven out of equilibrium by oscillations. The system tries to restore equilibrium through various internal processes. In the case of r-modes, since the typical frequencies are of the order of the rotational frequencies of the stars, the reactions that dominate are the weak processes. Within these weak processes, although the modified Urca processes involving leptons are important, it has been shown that non-leptonic processes involving hyperons contribute more significantly towards bulk viscosity at temperatures lower than a few times 109 K (Lindblom & Owen 2002). Our goal here is to investigate the same using a chiral model calibrated to reproduce the desired nuclear matter properties, in particular the density content of the nuclear symmetry energy at both low and high densities.

2020AandA...642A..90M__Werner_et_al._2006_Instance_1

Additional information can be derived from the abundance ratios measured in the ICM. For example, the Mn/Fe and Ni/Fe ratios are both sensitive to the electron capture rates during SNIa explosions, and are therefore crucial indicators of their progenitor channels (Seitenzahl et al. 2013a; Mernier et al. 2016a; Hitomi Collaboration 2017), while ratios of lighter elements can in principle provide constraints on the IMF and the initial metallicity of the SNcc progenitors (e.g. de Plaa et al. 2007; Mernier et al. 2016a). By pushing current observatories to their limit, recent studies derived constraints on the relative fraction of SN events that effectively contribute to the ICM enrichment. These measurements showed that SNIa and SNcc contribute relatively equally to the overall chemical enrichment in the ICM (Werner et al. 2006; de Plaa et al. 2006, 2017; Bulbul et al. 2012; Mernier et al. 2016a). The comprehensive study of Simionescu et al. (2019) compiled the most accurate abundance measurements of the Perseus cluster (taken with the XMM-Newton RGS and Hitomi SXS instruments) and compared them to state-of-the-art SNIa and SNcc yield models. Their surprising conclusion is that no current set of models is able to reproduce all the observed abundance ratios at once. In particular, the measured Si/Ar ratio tends to be systematically overpredicted by models, even when taking calibration and atomic uncertainties into account. Whereas further improvement of stellar nucleosynthesis models is expected, the non-negligible systematic errors associated to these observations and the lack of highly sensitive, spatially resolved high-resolution spectroscopy prevents us from steering any considerable change in the paradigm (see also de Grandi & Molendi 2009). In fact, measurements from currently flying missions are performed with moderate collective area, either over the whole X-ray band (0.4–10 keV) with modest spectral resolution (> 100 eV), or with higher resolution dispersive spectroscopy but over the low E band (0.3–2 keV) and without any spatial resolution, which considerably limits interpretations.

2016ApJ...825...10T__Brisken_et_al._2003_Instance_1

Although this penalty is small, we can still provide a quantitative estimate based on a few assumptions. The observations of M15 that measured the parallax to VLA J2130+12 (K14) employed an interferometric technique where the position of a weaker potential in-beam calibrator source (M15 S1 and VLA J2130+12) can be measured against a brighter primary calibration source, with the potential for using the in-beam calibrator to transfer more accurate calibration solutions to other in-beam targets. Based on a literature review for papers where VLBI parallax measurements were determined using an in-beam calibrator, we find 24 similar measurements, the majority of which have been used to measure parallaxes to pulsars (Fomalont et al. 1999; Brisken et al. 2003; Chatterjee et al. 2005, 2009; Ng et al. 2007; Middelberg et al. 2011; Deller et al. 2012, 2013; Ransom et al. 2014; Liu et al. 2016). As these measurements represent additional opportunities to detect the parallax of a VLA J2130+12-like object, they could be considered as potential trials. However, all of the reported in-beam calibrators were significantly brighter (4–86 mJy) than VLA J2130+12 (∼0.1–0.5 mJy). Given our conservative assumption of a uniform volume density of VLA J2130+12-like objects in the Galaxy, we should down-weight the number of trials from brighter sources by (fν,in-beam/fν,VLA J2130+12)−1.5. In that case, the number of trials penalty is only an additional ∼0.2 trials. In addition, we note that the PSRπ parallax project (a large VLBA program) has reported15 15 https://safe.nrao.edu/vlba/psrpi/ 111 additional in-beam calibrator sources that they used to measure parallaxes. Although they do not provide the flux densities of individual sources, they note that their median in-beam calibrator source is 9.2 mJy. We have measured the flux density function of secure 1–20 mJy FIRST sources (Helfand et al. 2015; ) to estimate the expected distribution of the flux densities of in-beam calibrators. We found that the minimum flux density is likely ∼3.2 mJy, and using the same down-weighting we estimate an additional penalty of ∼0.9 trials.

2016ApJ...833..192S__Champion_et_al._2008_Instance_1

In recent years, several large-scale pulsar surveys have been undertaken to search for new pulsars (Cordes et al. 2006; Keith et al. 2010; Barr et al. 2013; Boyles et al. 2013; Deneva et al. 2013; Coenen et al. 2014; Stovall et al. 2014). One of the drivers for such surveys is the discovery of millisecond pulsars (MSPs). MSPs are formed through accretion from a companion during an X-ray binary phase (Alpar et al. 1982; Bhattacharya & van den Heuvel 1991) in which the pulsar is “recycled.” This accretion phase spins the pulsar up to very fast rotational rates (spin periods ms). Such pulsars are useful for a variety of physical applications. Examples include tests of theories of gravity using MSP–white dwarf systems such as PSR J1738+0333 and PSR J0348+0432 (Freire et al. 2012; Antoniadis et al. 2013) and triple systems like PSR J0337+1715 (Ransom et al. 2014); tests of general relativity using double neutron star systems, such as J0737−3039 (Kramer et al. 2006) and PSR B1913+16 (Weisberg et al. 2010); the study of binary systems such as eccentric MSPs like PSRs J1903+0327 (Champion et al. 2008) and J1950+2414 (Knispel et al. 2015) which are interesting due to their peculiar binary evolution; and constraining the equation of state of dense matter using measurements of neutron star masses (Demorest et al. 2010; Antoniadis et al. 2013). Another major driver for the discovery of new MSPs is the effort to detect gravitational wave emission using an array of pulsars (NANOGrav Collaboration et al. 2015; Lentati et al. 2015; Reardon et al. 2016). The large-scale pulsar surveys mentioned above, combined with targeted searches of unidentified gamma-ray sources from the Fermi Gamma-Ray Space Telescope (e.g., Hessels et al. 2011; Keith et al. 2011; Ransom et al. 2011; Kerr et al. 2012), have resulted in the discovery of about 90 new MSPs in the past 5 years, an increase of 40% in the known Galactic MSP population. A subset of the newly discovered sources are eclipsing systems that appear to fall into two categories (e.g., Freire 2005; Roberts 2012). The first category, known as black widow systems, has very low mass, degenerate companions ( ) believed to be the result of ablation by the pulsar. The second, known as redback systems, has low to moderate mass, non-degenerate companions ( ).

2020AandA...641A.123H__Mikal-Evans_et_al._(2019)_Instance_1

To confirm the importance of VO, water and an inversion layer (Evans et al. 2018; Mikal-Evans et al. 2019, 2020) obtained repeated HST observations of the transmission spectrum and the secondary eclipse using the STIS and WFC 3 instruments. The optical transmission spectrum displays rich variation, with multiple features consistent with VO absorption that Evans et al. (2018) could reproduce by assuming an isothermal T-P profile at 1500 K and a metallicity equivalent to 10× to 30× solar. Absorption bands of TiO appeared to be muted in the transmission spectrum, which was explained by Evans et al. (2018) as evidence of condensation of Ti-bearing species, which commences at higher temperatures than condensation of V-bearing species, producing for example, calcium titanates (Lodders 2002) while VO remains in the gas phase. Mikal-Evans et al. (2019) observed the day side emission spectrum with the G102 grism of WFC3 (0.8–1.1 μm), augmenting their earlier observations with the G141 grism. The G102 spectrum does not show the VO bands expected to be present there, and this led Mikal-Evans et al. (2019) to question the interpretation that the 1.2 μm feature is caused by VO emission. The secondary eclipse was observed at 2 μm (Kovács & Kovács 2019) and at optical wavelengths with the TESS instrument. These were analysed together with the preceding Hubble, Spitzer, and ground-based observations to yield tighter constraints on the atmospheric structure, composition and overall system parameters (Bourrier et al. 2020a; Daylan et al. 2019). These studies found that the hottest point on the day side exceeds a temperature of 3000 K, that the atmosphere is inverted on the day side, and a metallicity that is consistent with solar (Bourrier et al. 2020a) or slightly elevated (Daylan et al. 2019). Although the chemical retrievals follow different strategies (equilibriumversus free-chemistry), both indicate that a depletion of TiO relative to VO is needed to explain the observed emission spectrum, supporting the earlier findings by Mikal-Evans et al. (2019). Recently, Mikal-Evans et al. (2020) obtained new secondary-eclipse observations using the G141 grism of WFC3. Although confirming the presence of emission by H2O, a joint analysis with their previous WFC3 observations did not reproduce the emission feature at 1.2 μm, prompting the authors to entirely discard their previous interpretation of emission caused by VO.

2020ApJ...902...98G__Bournaud_et_al._2014_Instance_1

On balance, a large abundance of baryon-dominated, dark matter cored galaxies at z ∼ 2, most strongly correlated with baryonic surface density, angular momentum, and central bulge mass, may be most naturally accounted for by the interaction of baryons and dark matter during the formation epoch of massive halos. Massive halos (log(Mhalo/M⊙) > 12) formed for the first time in large abundances in the redshift range z ∼ 1–3 (Press & Schechter 1974; Sheth & Tormen 1999; Mo & White 2002; Springel et al. 2005). At the same time, gas accretion rates were maximal (Tacconi et al. 2020). This resulted in high merger rates (Genel et al. 2008, 2009; Fakhouri & Ma 2009), very efficient baryonic angular momentum transport (Dekel et al. 2009; Zolotov et al. 2015), formation of globally unstable disks, and radial gas transport by dynamical friction (Noguchi 1999; Immeli et al. 2004; Genzel et al. 2008; Bournaud & Elmegreen 2009; Bournaud et al. 2014; Dekel & Burkert 2014). These processes enabled galaxy mass doubling on a timescale 0.4 Gyr at z ∼ 2–3, and massive bulge formation by disk instabilities and compaction events on 1 Gyr timescales. However, central baryonic concentrations would naturally also increase central dark matter densities through adiabatic contraction (Barnes & White 1984; Blumenthal et al. 1986; Jesseit et al. 2002). For adiabatic contraction to be ineffective requires the combination of kinetic heating of the central dark matter cusp by dynamical friction from in-streaming baryonic clumps (El-Zant et al. 2001; Goerdt et al. 2010; Cole et al. 2011), with feedback from winds, supernovae, and AGNs driving baryons and dark matter out again (Dekel & Silk 1986; Pontzen & Governato 2012, 2014; Martizzi et al. 2013; Freundlich et al. 2020; K. Dolag et al. 2020, in preparation). Using idealized Monte Carlo simulations, El-Zant et al. (2001) demonstrated that dynamical friction acting on in-spiraling gas clumps can provide enough energy to heat up the central dark matter component and create a finite dark matter core (see also A. Burkert et al. 2020, in preparation). They argue that dark matter core formation in massive galaxies would require that clumps be compact, such that they avoid tidal and ram-pressure disruption, and have masses of >108 M⊙. Other idealized simulations (e.g., Tonini et al. 2006) confirm these results.

2022MNRAS.512.4136C__Ventura_et_al._2013_Instance_1

If we recall the tight, monotonic dependence of the position of galaxies along the SF sequence in the diagram with metallicity (as outlined in Section 3.1), we can interpret our global results of Figs 4 and 5 as a manifestation of the existence of an O/H versus N/O relation for SDSS star-forming galaxies, whose intrinsic scatter is reflected and, to some extent, translated into the observed distribution of emission line ratios within the [N ii]-BPT. A tight relationship between O/H and N/O abundances is indeed observed in both H ii regions and local galaxies, especially at M⋆ ≳ 109.5M⊙ (Vila Costas & Edmunds 1993; van Zee et al. 1998; Pérez-Montero & Contini 2009; Pilyugin et al. 2012; Andrews & Martini 2013; Hayden-Pawson et al. 2021), and it is set by the predominant nucleosynthetic origin of nitrogen from CNO burning of pre-existing stellar carbon and oxygen in low- and intermediate-mass stars experiencing the AGB phase (i.e. the ‘secondary’ nitrogen production mechanism, Kobayashi, Karakas & Umeda 2011; Ventura et al. 2013; Vincenzo et al. 2016); alternatively, Vincenzo & Kobayashi (2018) reproduced the observed N/O-O/H relation introducing failed supernovae (SNe) in massive stars within their cosmological simulations. Recently, such relationship between O/H and N/O has been suggested as even tighter than the one between M⋆ and N/O (Hayden-Pawson et al. 2021), in contrast to what claimed by previous studies (e.g. Andrews & Martini 2013; Masters et al. 2016). In light of our results, this would confirm that deviations in N/O at fixed O/H are more likely to be related to the offset from the SF sequence in the [N ii]-BPT than relative variations in M⋆, although the two are clearly physically correlated. The connection between the two diagrams is also readily evident if we look at the distribution of our galaxy sample in the N/O versus O/H diagram, as shown in Fig. 8 (where [N ii] λ6584/[O ii] λ3727, 29 is converted to N/O following the Te-based calibrations presented in Hayden-Pawson et al. 2021); here, each hexagonal bin is colour-coded by the average distance D of galaxies from the best-fitting line of the [N ii]-BPT, almost perfectly tracing the scatter around the median N/O versus O/H relation.

2021ApJ...912..163B__Brennecka_et_al._2020_Instance_1

Braukmuller et al. (2018) proposed that all elements fall into one of four categories based on their condensation temperature: refractory elements (50% condensation temperature, Tc,50 > 1400 K), which exhibit approximately uniform enrichments in their Si-normalized concentrations in CC chondrites compared to CI chondrites by a factor of ∼1–1.4; main component elements (1300 K Tc,50 1400 K), which have approximately the same Si-normalized elemental abundances in CC chondrites as CI chondrites (differ by a factor of ∼0.8–1.1); slope-volatile elements (800 K Tc,50 1300 K), which exhibit monotonically decreasing Si-normalized concentrations with decreasing Tc,50 compared to CI chondrites; and plateau volatile elements (Tc,50 800 K), which display uniform depletions in Si-normalized concentrations compared to CI chondrites by a factor of ∼0.1–0.7 that are characteristic of each CC chondrite group. Given their uniform nature with Tc,50 and comparatively well-constrained isotopic and elemental compositions, we chose to focus on the concentrations of refractory, main component, and plateau volatile elements in this study. For the refractory and main component elements in CC chondrites, we examine the elemental and isotopic compositions of Ti and Cr, respectively, because these are lithophile elements whose isotopic compositions have been measured precisely for a large number of chondrites and their components (Trinquier et al. 2007, 2009; Qin et al. 2010; Olsen et al. 2016; Van Kooten et al. 2016; Gerber et al. 2017; Davis et al. 2018; Zhu et al. 2019; Schneider et al. 2020; Williams et al. 2020). For CC iron meteorites, we examine the isotopic compositions of Mo and Ni, respectively, because these are siderophile elements (so are therefore present in appreciable concentrations in iron meteorites, unlike Ti and Cr) whose compositions have also been relatively well studied in a number of iron meteorites as well as chondrites and their components (Burkhardt et al. 2011; Budde et al. 2016; Kruijer et al. 2017; Bermingham et al. 2018; Nanne et al. 2019; Budde et al. 2019; Worsham et al. 2019; Brennecka et al. 2020; Spitzer et al. 2020). For the plateau volatile elements, we examine the elemental compositions of six elements (Bi, Ag, Pb, Zn, Te, and Sn) that exhibit a number of desirable properties: their concentrations have been relatively well constrained in CC chondrites; they show a range of lithophile, siderophile, and chalcophile behaviors; their concentrations do not appear to be strongly dependent on redox state; they show minimal variability among NC chondrite groups. Our reasoning for not considering the isotopic compositions of these elements is discussed in Section 2.3. The adopted isotopic and chemical composition of each element used in this study in CC chondrites, CC iron meteorites, CAIs, CI chondrites, and NC chondrites are included in Table 1. Uncertainties on elemental concentrations have not been routinely reported throughout the literature, although these values are typically ±5 wt% (e.g., Lodders 2003; Palme et al. 2014). CAIs can be categorized into six groups based on their compositions (Stracke et al. 2012). For the purposes of this study, we adopt the composition of type I CAIs as the representative value of refractory objects because they are seemingly the most abundant type and lack the characteristic elemental depletions of other CAI groups (e.g., Stracke et al. 2012; Brennecka et al. 2020). We also focus largely on ordinary chondrites (OC) as representative NC meteorites rather than enstatite chondrites (EC) or Rumuruti chondrites (RC). This is because EC chondrites formed under more reducing conditions than OC and RC chondrites, which introduced a compositional signature for some elements to EC chondrites that is not present in OC, RC, or CC chondrites (presumably due to their formation in more oxidizing environments) so is not representative of large-scale mixing in the disk (Alexander 2019b). Additionally, the isotopic compositions of RC chondrites are sparsely measured compared to OC and EC chondrites. NC meteorites could have experienced a number of processes (e.g., mixing, chondrule formation, volatile loss, the addition of refractory materials, etc.) that gave these meteorites their specific chemical and isotopic signatures (Alexander 2019b). We do not explore these processes in this study and simply adopt the measured elemental and isotopic compositions of NC chondrites as potential end-members for the compositions of CC meteorites.

2015MNRAS.449.4326P__Collaboration_2006_Instance_1

We now consider upcoming spectroscopic surveys. We consider two cases for the CMB lensing map, including (1) the full Planck CMB lensing map and (2) the Advanced ACTPol5 CMB lensing map. In both cases, we assume that the CMB lensing maps will be estimated using the temperature map and both E and B polarization maps, and we assume the B map only contains noise. We predict the noise in the Planck lensing map assuming the detector sensitivity and beam sizes listed in the Planck Bluebook (Planck Collaboration 2006). Advanced ACTPol will survey 20 000 deg2, and its increased temperature and polarization sensitivity will create a CMB lensing map that is an order of magnitude more sensitive than Planck. The specifications we use for Advanced ACTPol are listed in Table 3. For spectroscopic surveys, we consider the DESI emission line galaxy (ELG), LRG, and quasar surveys, as well as the Euclid Hα survey and the WFIRST Hα and O iii combined survey. The properties of the surveys are listed in Table 1. For DESI, we assume the same values as in the DESI Conceptual Design Report:6 bLRGD(z) = 1.7, bELGD(z) = 0.84, bQSOD(z) = 1.2, where D(z) is the growth factor. We also assume a 4 per cent error in β within Δz = 0.1 bins. Note that Advanced ACTPol's survey area overlaps with only ∼75 per cent of DESI's area; we take this into account in our DESI forecasts. For Euclid and WFIRST ELGs, we assume b(z) = 0.9 + 0.4z, a fit (Takada et al. 2014) to semi-analytic models (Orsi et al. 2010) that compares well with data. We determine the redshift distribution of Euclid Hα galaxies using the Hα luminosity function from Colbert et al. (2013) and assume a flux limit of 4× 10−16. This flux limit is in the middle of the range being considered, so the following Euclid forecasts can change accordingly. We also assume a 3 per cent error in β within Δz = 0.1 bins for Euclid and WFIRST (Amendola et al. 2013). For all subsequent forecasts, we assume EG measurements over angular scales 100 ≤ ℓ ≤ 500.

2022MNRAS.510.4943S__Murray_&_Dermott_1999_Instance_2

The gravitational potential of an eccentric companion at the quadrupole order can be decomposed as a sum over circular orbits (e.g. Storch & Lai 2013; Vick, Lai & Fuller 2017): (5)$$\begin{eqnarray*} U\left(\boldsymbol{\mathbf {r}}, t\right) = \sum \limits _{m=-2}^2 U_{2m} \left(\boldsymbol{\mathbf {r}}, t\right) , \end{eqnarray*}$$(6)$$\begin{eqnarray*} U_{2m}\left(\boldsymbol{\mathbf {r}}, t\right) &=& -\frac{GM_2 W_{2m} r^2}{D(t)^3} Y_{2m}(\theta , \phi) e^{-imf\!\!\!\:(t)}, \\ &=& -\frac{GM_2W_{2m} r^2}{a^3}Y_{2m}\left(\theta , \phi \right) \sum \limits _{N = -\infty }^\infty \!\!F_{Nm}e^{-iN\Omega t} . \end{eqnarray*}$$Here, the coordinate system is centered on the MS star, (r, θ, ϕ) are the radial, polar, and azimuthal coordinates of $\boldsymbol{\mathbf {r}}$ respectively, $W_{2 \pm 2} = \sqrt{3\pi /10}$, W2 ± 1 = 0, $W_{20} = -\sqrt{\pi / 5}$, D(t) is the instantaneous distance to the companion, f is the true anomaly, and Ylm denote the spherical harmonics. FNm denote the Hansen coefficients for l = 2 (also denoted $X^N_{2m}$ in Murray & Dermott 1999), which are the Fourier coefficients of the perturbing function, i.e. (7)$$\begin{eqnarray*} \frac{a^3}{D(t)^3} e^{-imf\!\!\!\:(t)} = \sum \limits _{N = -\infty }^\infty \!\!F_{Nm} e^{-iN\Omega t}. \end{eqnarray*}$$The FNm can be written explicitly as an integral over the eccentric anomaly (Murray & Dermott 1999; Storch & Lai 2013): (8)$$\begin{eqnarray*} F_{Nm} = \frac{1}{\pi }\int \limits _{0}^{\pi } \frac{\cos \left[N\left(E - e\sin E\right) - mf(E)\right]}{\left(1 - e\cos E\right)^2}\,\,\mathrm{d}E. \end{eqnarray*}$$By considering the effect of each summand in equation (5), the total torque on the star, energy transfer in the inertial frame, and energy transfer in the star’s corotating frame (which is also the tidal heating rate) can be obtained (Storch & Lai 2013; Vick et al. 2017): (9)$$\begin{eqnarray*} T = \sum \limits _{N = -\infty }^\infty F_{N2}^2 T_{\rm circ}\left(N\Omega - 2\Omega _{\rm s}\right), \end{eqnarray*}$$(10)$$\begin{eqnarray*} \dot{E}_{\rm in} &=& \frac{1}{2}\sum \limits _{N = -\infty }^\infty \Bigg [ \left(\frac{W_{20}}{W_{22}}\right)^2 N\Omega F_{N0}^2 T_{\rm circ}\left(N\Omega \right) \\ &&+\, N\Omega F_{N2}^2 T_{\rm circ}\left(N\Omega - 2\Omega _{\rm s}\right) \Bigg ] , \end{eqnarray*}$$(11)$$\begin{eqnarray*} \dot{E}_{\rm rot} = \dot{E}_{\rm in} - \Omega _{\rm s} T . \end{eqnarray*}$$Here, dots indicate time derivatives.

2018MNRAS.478..932H__Anjos_2001_Instance_1

Although a correlation does exist, it is offset from the one-to-one line that one would expect, overestimating the number of spiral arms by approximately three. This may be due to how mass is assigned to the bulge and disc. We use photometric decompositions of Simard et al. (2011) and Mendel et al. (2014) to assign mass to the bulge and the disc. Such a model fits a classical bulge with n= 4 and an exponential disc. This may cause a systematic for two reasons. First, the photometric decomposition of galaxies may introduce a bias due to image resolution effects. The second issue is the pseudo- versus classical bulge argument – the model we use assumes an inner classical spherical bulge; bulges instead may be pseudo-bulges, which may not have a spherical shape, and profile well-described by a spherical Hernquist profile (Carollo et al. 1997; Gadotti & dos Anjos 2001; Kormendy et al. 2006; Fisher & Drory 2008; Gadotti 2009). Studying bulges and discs in detail is beyond the scope of this paper. Another possibility is that the assumption that spiral arms are measured at 2Rd may not be valid – if spiral arms were instead measured closer to the inner regions of galaxies, then this offset is negated. Unfortunately, the binary nature of visual morphological classifications, where arms either are or are not recorded, prevents further investigation of this point. Finally, there may be some spiral arms which are impossible to observe with visual morphology in the way presented in this paper. Of particular note is the case where the model predicts very high spiral arm numbers. In this case, the spiral arms may instead be wakelets which are difficult to observe visually; our observed arm number measurements may therefore be systematically low for these galaxies. Investigating which caveat, or which combination of caveats is responsible requires higher resolution imaging of galaxies than those used in this paper. Any study of this nature would be severely restricted in terms of sample size and completeness compared to the results we present in this paper.

2015ApJ...806..152S__Ferraro_et_al._2001_Instance_1

One of the most astonishing characteristics of Liller 1 is the extremely large value of the collision rate parameter. Verbunt & Hut (1987) showed that Liller 1 has the second-highest value of stellar encounter rate (after Terzan 5; see also Lanzoni et al. 2010) among all star clusters in the Galaxy, thus suggesting that it represents an ideal environment where exotic objects, generated by collisions, can form. In fact, it is commonly believed that dynamical interactions in GCs facilitate the formation of close binary systems and exotic objects such as cataclysmic variables (CVs), low-mass X-ray binaries (LMXBs), millisecond pulsars (MSPs), and blue straggler stars (BSSs) (e.g., Bailyn 1992; Paresce et al. 1992; Ferraro et al. 2001, 2009a, 2012; Ransom et al. 2005; Pooley & Hut 2006). Moreover, Hui et al. (2010) found that clusters with large collisional parameters and high metallicity (see also Bellazzini et al. 1995) usually host more MSPs. Indeed, Terzan 5 hosts the largest population of MSPs among all Galactic GCs (Ransom et al. 2005). 6 6 Note that Terzan 5 is suspected to not be a genuine GC, because it harbors at least three stellar populations with different iron abundances (Ferraro et al. 2009a; Origlia et al. 2011, 2013; Massari et al. 2014). A strong γ-ray emission has been recently detected in the direction of Liller 1 by the Large Area Telescope (LAT) on board Fermi (Tam et al. 2011). This is the most intense emission detected so far from a Galactic GC, again suggesting the presence of a large number of MSPs. However, no direct radio detection of these objects has been obtained so far in this system (Ransom et al. 2005). The only exotic object identified in the cluster is the rapid burster MXB 1730-335, an LMXB observed to emit radio waves and type I and type II X-ray bursts (Hoffman et al. 1978). It seems to be located in the central region of Liller 1, but no optical/IR counterpart of this object has been found so far (Homer et al. 2001).

2022MNRAS.511.1121M__Reig_&_Nespoli_2013_Instance_1

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

2022MNRAS.515.2188H__Rorai_et_al._2018_Instance_4

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

2017ApJ...838...67E__Derekas_et_al._2017_Instance_1

In the present era of “high precision” cosmology (see Riess et al. 2016), it is important to exploit the full potential of Cepheids as precise extragalactic distance indicators for determining the expansion rate of the universe and setting constraints on cosmology models. To achieve these goals, a deeper understanding and characterization of Cepheids is needed. Recent discoveries such as circumstellar environments (Nardetto et al. 2016), infrared excesses (Mérand et al. 2015), and ultraviolet emission line variability and possible more recent X-ray emissions (Engle et al. 2014) show that some important aspects of Cepheids may not be well understood. Cepheids have also been found to show additional complications that include cycle-to-cycle variations in their light and radial velocity curves (see Evans et al. 2015b; Anderson 2016; Anderson et al. 2016; Smolec & Śniegowska 2016; Derekas et al. 2017). These newly discovered properties and time-dependent phenomena of Cepheids, unless better understood and accounted for, could place impediments on achieving the challenging goal of determining the local Hubble constant (H0) with a precision of ∼1%, as suggested by Suyu et al. (2012). Great efforts are being undertaken to achieve this level of precision, and hopefully resolve the developing “Hubble Discrepancy” (see Riess et al. 2016), where theoretical values of the Hubble constant (H0) derived via the Lambda-cold dark matter (Λ-CDM) cosmology model (Λ = the cosmological constant), including cosmic microwave background data (e.g., Planck, WMAP (Wilkinson Microwave Anisotropy Probe)), show a small (albeit statistically significant) disagreement with the value of H0 will be derived via standard candles (e.g., Cepheids, SNe Ia (type Ia Supernovae)). As discussed by Suyu et al. (2012) and more recently by Riess et al. (2016), improved measurements of H0 provide critical independent constraints on dark energy and the validity of the present Λ-CDM model.

2018MNRAS.474.3280F__Sotomayor-Beltran_et_al._2013_Instance_1

The ability to retrieve the polarized quantities of a radio source is entirely dependent on the ability to find radio sources within noisy images. It is of importance to planned future surveys to investigate suitable strategies for source-finding in linear polarization with interferometers such as LOFAR, which are very well suited for deep radio surveys (e.g. Hardcastle et al. 2016; Clarke et al. 2017), but are particularly technically challenging due to operation at low radio frequencies (Varenius et al. 2015), with potentially sub-arcsecond angular resolution (Moldón et al. 2015), at high sensitivity (Shimwell et al. 2016), and with the ability to make precise Faraday rotation measurements (Sotomayor-Beltran et al. 2013). Nevertheless, finding linearly polarized sources faces many hurdles: (i) at sub-arcminute resolution, the peak in linearly polarized intensity can be offset from the peak in total intensity (see e.g. fig. 1 in O'Sullivan et al. 2015), (ii) the statistics in polarized intensity, $P=\sqrt{Q^2+U^2}$, are Rician, rather than Gaussian, while all publicly available source-finders are geared towards Gaussian noise statistics, (iii) the full sensitivity is not provided in any single channel of Q, U, or P, and RM Synthesis is therefore required to retrieve the full point-source sensitivity from the data, (iv) sources detected in Q and U can have both positive and negative brightness, and these values oscillate and mix across the observing bandwidth due to Faraday rotation, and (v) in some cases, Q and U images can be more sensitive than I images, which in principle could lead to sources that can be found in P but not in I. Source-finding in circular polarization, Stokes V, is beyond the scope of this paper in which we focus on linear polarization, but also faces similar challenges due to the full sensitivity not being provided in a single channel and the process of Faraday conversion across the observing band. Moreover, the linear feeds used for observations at low radio frequencies with instruments such as LOFAR are more suitable for measuring circular rather than linear polarization, which further increases the difficulty of detecting faint linearly polarized sources.

2017ApJ...837...88B__High_et_al._2010_Instance_1

With photometry in hand for the majority of our galaxy spectroscopy sample, we can also investigate velocity segregation effects as a function of galaxy luminosity. We use cluster galaxy brightness measurements in units relative to m*, a standard quantity that can be easily incorporated into both observational data and simulated clusters (Cole et al. 2001; Cohen 2002; Rudnick et al. 2006, 2009). Specifically, we use m* values computed from Bruzual & Charlot (2003) models in the same fashion as described in previous SPT publications (High et al. 2010; Song et al. 2012; Bleem et al. 2015). Figure 7 shows the distribution of cluster member galaxies with brightness measurements as described in Section 2.4 plotted relative to the characteristic magnitude, m*, for the full cluster member sample as well as the subsamples of cluster members of different spectral types. We use these data, in combination with the normalized peculiar velocities of each cluster member in the ensemble to plot the expectation value for the absolute peculiar velocity of cluster members as a function of brightness for all galaxies together, as well as for each of the passive, post-starburst, and star-forming galaxy subsets (Figure 8). It is clear that the brightest galaxies—independent of spectral type—universally prefer smaller absolute peculiar velocities, and that when we treat all galaxies together we see a strong drop in the absolute peculiar velocities of galaxies brighter than , while galaxies fainter than this tend to remain approximately flat in absolute peculiar velocity as a function of brightness. There is no statistically significant evidence in our data for an evolution in the presence or of velocity luminosity segregation with redshift. Specifically, if we split our sample into two redshift bins at z = 0.45—which optimally balances the number of bright galaxies in the high and low bins—we see the strong drop in peculiar velocity for bright galaxies in each of the high and low-redshift bins.

2017AandA...602A.106B__Ehrenreich_et_al._2015_Instance_1

About a quarter of the known exoplanets orbit at short distances (≲0.1 au) from their star (from the Exoplanet Encyclopaedia in December 2016; Schneider et al. 2011). Heating by the stellar energy can lead to the expansion of their upper atmospheric layers and their eventual escape. As a result of this expansion, the upper atmosphere produces a deeper absorption than the planetary disk alone when observed in the UV, in particular in the stellar Lyman-α (Ly-α) line of neutral hydrogen (e.g., Vidal-Madjar et al. 2003; Lecavelier des Etangs et al. 2012). Super-Earths and smaller planets display a large diversity in nature and composition (e.g., Seager et al. 2007; Rogers & Seager 2010; Fortney et al. 2013), which can only be investigated through observations of their atmospheres. The reduced scale height of the lower atmospheric layers makes them difficult to probe in the visible and the infrared. In contrast, very deep UV transit signatures can be produced by the upper atmospheres of small planets. The warm Neptune GJ436b, which is the lowest mass planet found evaporating to date (Ehrenreich et al. 2015), shows transit absorption depths up to 60% in the Ly-α line. The formation of such an extended exosphere is due in great part to the low mass of GJ436b and the gentle irradiation from its M-dwarf host (Bourrier et al. 2015, 2016b). Few attempts have been made to detect atmospheric escape from Earth-size planets, and Ly-α transit observations of the super-Earth 55 Cnc e (Ehrenreich et al. 2012) and HD 97658 b (Bourrier et al. 2016a) showed no evidence of hydrogen exospheres. In the case of 55 Cnc e, this non-detection hinted at the presence of a high-weight atmosphere – or the absence of an atmosphere – recently supported by the study of its brightness map in the IR (Demory et al. 2016). Understanding the conditions that can lead to the evaporation of Earth-size planets will be necessary to determine the stability of their atmospheres and their possible habitability. For example, large amounts of hydrogen in the upper atmosphere of a close-in terrestrial planet could indicate the presence of a steam envelope being photodissociated, and replenished by evaporating water oceans (Jura 2004; Léger et al. 2004).

2015MNRAS.451.2663H___2005_Instance_1

Galaxy formation theory has developed dramatically over the last three decades. Λ cold dark matter (ΛCDM) has been established as the standard model for cosmological structure formation, and its parameters have been increasingly tightly constrained by observations. In parallel, simulations of galaxy formation within this standard model have grown in complexity in order to treat more accurately the many baryonic processes that impact the evolution of the galaxy population. Semi-analytic modelling is a particular simulation method which is optimized to connect the observed properties of the galaxy population – abundances, scaling relations, clustering and their evolution with redshift – to the astrophysical processes that drive the formation and evolution of individual galaxies (e.g. White 1989; Cole 1991; Lacey & Silk 1991; White & Frenk 1991; Kauffmann, White & Guiderdoni 1993; Cole et al. 1994; Kauffmann et al. 1999; Somerville & Primack 1999; Springel et al. 2001, 2005; Hatton et al. 2003; Kang et al. 2005; Lu et al. 2011; Benson 2012). Simple phenomenological descriptions of the relevant processes are needed, each typically involving uncertain efficiency and scaling parameters. These must be determined by comparison with observation or with more detailed simulations. As the range and quality of observational data have increased, so has the number of processes that must be included to model them adequately, and hence the number of adjustable parameters. In recent years, robust statistical methods have been introduced in order to sample the resulting high-dimensional parameter spaces and to determine the regions that are consistent with specific observational data sets. This development began with the work of Kampakoglou, Trotta & Silk (2008) and Henriques et al. (2009) and has since been extended to a wide range of models and sampling methods (Benson & Bower 2010; Bower et al. 2010; Henriques & Thomas 2010; Lu et al. 2011, 2012; Henriques et al. 2013; Mutch, Poole & Croton 2013; Benson 2014; Ruiz et al. 2015).

2020ApJ...897..158S__Sherwood_et_al._2003_Instance_1

For the study of each of the individual reactions we have characterized the stationary points on their respective potential energy surfaces by means of density functional theory (DFT) calculations. We have employed the mPWB1K exchange and correlation functional of Zhao & Truhlar (2004) in combination with the def2-TZVP basis set (Weigend & Ahlrichs 2005). The main advantage of this functional for this investigation is that it was designed for the easy determination of activation energies. Minima and transition states (TSs) were optimized using the DL-FIND (Kästner et al. 2009) program of the ChemShell suite (Sherwood et al. 2003; Metz et al. 2014). A search of TS was done by means of potential energy surface scans and posterior optimization in cases where a kinetic barrier was predicted. Assessment of the nature of the stationary points was performed by computing the molecular Hessian of each. Additionally, we have addressed the validity of the DFT method by computing single-point calculations on the relevant DFT geometries at the CCSD(T)-F12/cc-PVTZ-F12 level of theory using Molpro 2015 (Werner et al. 2012, 2015). We have found excellent agreement between DFT and coupled cluster methods for the reaction barriers, with deviations of less than 1 kcal mol−1 in all cases. Furthermore, we have carried out intrinsic reaction coordinate (IRC) calculations to ensure a proper connection between our calculated TSs and their associated minima. For reactions with a barrier, we have computed both classical bimolecular reaction rate coefficients as well as tunneling-corrected ones. The inclusion of quantum tunneling in our calculations has been performed using semi-classical instanton theory (Rommel & Kästner 2011; Rommel et al. 2011), following a sequential cooling scheme for temperatures below the crossover temperature, and reduced instanton theory for temperatures above (McConnell & Kästner 2017). Crossover temperatures are defined—with being the frequency of the vibrational imaginary mode in the TS, and kB the Boltzmann constant—as 6

2022ApJ...925...37W__Lentati_et_al._2015_Instance_1

The stochastic gravitational-wave background (SGWB)—the primary goal of the search of the PTA collaborations—is expected to be dominant in the nanohertz band, which might originate from supermassive black hole binaries (SMBHBs; Rajagopal & Romani 1995; Sesana 2013), comic strings (Damour & Vilenkin 2005; Blanco-Pillado et al. 2018), the first phase transition (Caprini et al. 2010), and scalar-induced GWs (Yuan et al. 2019). Over the last few decades, the PTA collaborations have not found GW signals, but the increasingly sensitive data sets offer increasingly stringent constraints on the SGWB (van Haasteren et al. 2011; Shannon et al. 2013; Lentati et al. 2015; Shannon et al. 2015; Arzoumanian et al. 2016, 2018; Chen et al. 2020). Recently, the NANOGrav collaboration reported that there is strong evidence in favor of a stochastic common-spectrum process, which is modeled by a power-law spectrum among the pulsars, over the independent red noise processes of each pulsar, in their 12.5 yr data set (Arzoumanian et al. 2020). However, given the lack of statistically significant evidence for quadrupolar spatial correlations, it is inconclusive to claim the detection of an SGWB consistent with general relativity. Note that the tensor transverse (TT) modes giving rise to quadrupolar spatial correlations constitute only two of the six GW polarization modes that are allowed in a general metric theory of gravity, which also includes one scalar transverse (ST) mode, two vectorial longitudinal (VL) modes, and one scalar longitudinal (SL) mode. Later on, Chen et al. (2021) searched for nontensorial SGWBs in the NANOGrav 12.5 yr data set, and found strong Bayesian evidence that the common-spectrum reported by the NANOGrav collaboration had ST spatial correlations. More recently, the PPTA collaboration has also found a common-spectrum process in their second data release (DR2), with PPTA DR2 showing no significant evidence for, or against, TT spatial correlations (Goncharov et al. 2021a).

2022ApJ...928L..16Y__Yang_&_Zhang_2018_Instance_1

Fast radio bursts (FRBs) are cosmological radio transients with millisecond durations. Since the first FRB (FRB 010724, the Lorimer burst) was discovered in 2007 (Lorimer et al. 2007), hundreds of FRB sources have been detected, dozens of which are repeaters (e.g., the CHIME/FRB Collaboration et al. 2021). Recently, a Galactic FRB, FRB 200428, was detected to be associated with SGR J1935+2154 (Bochenek et al. 2020; CHIME/FRB Collaboration et al. 2020; Mereghetti et al. 2020; Li et al. 2021a; Ridnaia et al. 2021; Tavani et al. 2021), which suggests that at least some FRBs originate from magnetars born from the core collapse of massive stars (e.g., Popov & Postnov 2013; Katz 2016; Murase et al. 2016; Beloborodov 2017; Kumar et al. 2017; Yang & Zhang 2018, 2021; Metzger et al. 2019; Lu et al. 2020; Margalit et al. 2020; Wadiasingh et al. 2020; Wang et al. 2022; Zhang 2022). However, FRB 20200120E was found to be in a globular cluster of a nearby galaxy, M81 (Bhardwaj et al. 2021; Kirsten et al. 2022). This is in tension with the scenario that invokes active magnetars with ages ≲10 kyr formed in core-collapse supernovae (Kremer et al. 2021; Lu et al. 2022) and suggests that FRBs might originate from magnetars formed in compact binary mergers (Margalit et al. 2019; Wang et al. 2020; Zhong et al. 2020; Zhao et al. 2021). Therefore, the physical origin of FRBs is still not well constrained from the data (e.g., Cordes & Chatterjee 2019; Petroff et al. 2019; Zhang 2020; Xiao et al. 2021). The growing FRB detections start to shed light on the diversity among the phenomena. The repeaters presented in the first CHIME FRB catalog have relatively larger widths and narrower bandwidths compared with one-off FRBs (Pleunis et al. 2021). The behaviors of fluence with respect to peak flux exhibit statistically significant differences between bursts with long and short durations (Li et al. 2021c). Multiple origins for the FRB population seem increasingly likely.

2019MNRAS.482.3550K__Weisz_et_al._2013_Instance_1

The simplest approach to handling the problem of completeness is to be extremely conservative, and discard all data in regions of parameter space where the observations are not complete or nearly so. However, this invariably requires one to discard much of the available data. A somewhat more sophisticated approach is forward modelling: rather than deriving the mass and age distribution of the population from estimates of mass age for individual clusters, one could instead consider a proposed distribution of masses and ages, predict the resulting photometry distribution including the effects of incompleteness, and adjust parameters of the mass and age distribution until they match the observations. Approaches of this type are widely used in astronomy, for example to infer star formation histories or stellar mass distributions from observed colour–magnitude diagrams (CMDs; e.g. Dolphin 2002; Harris & Zaritsky 2009; Weisz et al. 2013; Conroy & van Dokkum 2016; see Cerviño 2013 for a review). However, methods of this type have not previously been applied to deriving the properties of populations of star clusters, at least in part due a unique challenge not present in other applications. In existing applications such as CMD fitting, the forward model is deterministic, i.e. for a given stellar mass, age, and other properties, there is a single predicted colour and magnitude. This is not the case for star clusters with masses ≲ 3000 M⊙, because such clusters are too small to fully sample the stellar initial mass function (IMF, e.g. Cerviño & Luridiana 2004, 2006; da Silva, Fumagalli & Krumholz 2012). As a result, two clusters of the same total mass and age can produce wildly different luminosities and colours. This means that the forward model is not deterministic, but instead depends on an additional random variable that couples non-linearly with the deterministic variables like cluster mass and age. This situation presents computational challenges that are not addressed by existing methods.

2019ApJ...871..243Y__Zhou_et_al._1993_Instance_1

B335 is an isolated Bok globule with an embedded Class 0 protostar at a distance of 100 pc (Keene et al. 1980, 1983; Stutz et al. 2008; Olofsson & Olofsson 2009). The size of the dense core in B335 observed at millimeter wavelengths is ∼0.1 pc (Saito et al. 1999; Motte & André 2001; Shirley et al. 2002), and the core is slowly rotating (Saito et al. 1999; Yen et al. 2011; Kurono et al. 2013). Infalling and rotational motions on scales from 100 to 3000 au have been observed in molecular lines with single-dish telescopes and interferometers (Zhou et al. 1993; Choi et al. 1995; Zhou 1995; Saito et al. 1999; Evans et al. 2005, 2015; Yen et al. 2010, 2011, 2015b; Kurono et al. 2013). Nevertheless, no sign of Keplerian rotation was observed with the Atacama Large Millimeter/submillimeter Array (ALMA) at an angular resolution of 03 (30 au; Yen et al. 2015b), and the envelope rotation on a scale of 100–1000 au in B335 is an order of magnitude slower than in other Class 0 and I protostars surrounded by a Keplerian disk with a size of tens of au (Yen et al. 2015a). The presence of a small disk less than 10 au and the slow envelope rotation hints at the effects of the magnetic field on the gas kinematics in B335. In addition, ALMA observations in the C18O and H13CO+ lines show no detectable difference in the infalling velocities of neutral and ionized gas on a 100 au scale with a constraint on the upper limit of the ambipolar drift velocity of 0.3 km s−1, suggesting that the magnetic field likely remains well coupled with the matter in the inner envelope in B335 (Yen et al. 2018). The magnetic field structures on a 1000 au scale in B335 also show signs of being dragged toward the center and becoming pinched, as inferred from the ALMA polarimetric observations (Maury et al. 2018). Therefore, B335 is an excellent target to investigate the interplay between the magnetic field and gas motions and the effects of the magnetic field on the dynamics in collapsing dense cores.

2016MNRAS.455..449H__Angus_et_al._2012_Instance_1

The EFE mentioned in the previous section is due to the fact that the MOND equations (3) and (4) are non-linear and involve the total gravitational acceleration with respect to a pre-defined frame (e.g. the CMB frame). Decomposing the total gravitational field ∇Φ into an internal part $\boldsymbol g$ and an external field $\boldsymbol g_{\rm e}$ and using a similar decomposition for the Newtonian gravitational acceleration ($\nabla \boldsymbol \Phi _{\rm N}=\boldsymbol g_{\rm N}+\boldsymbol g_{{\rm Ne}}$) allows us to solve the equations by taking into account the external field. This must typically be done with a numerical Poisson solver (Wu et al. 2008; Angus et al. 2012; Lüghausen, Famaey & Kroupa 2015). Nevertheless, fits to rotation curves in MOND usually neglect the small corrections due to the non-spherical symmetry of the problem, in order to allow for a direct fit of the rotation curve. In the same spirit, and in order to get a first glimpse of the influence of the EFE on rotation curves, we generalize the one-dimensional solution, by using the following formula to fit rotation curves, namely equation (60) from Famaey & McGaugh (2012): (6) \begin{equation} \boldsymbol g=\nu \left(\frac{|\boldsymbol g_{\rm N}+\boldsymbol g_{{\rm Ne}}|}{a_0}\right)\left(\boldsymbol g_{\rm N} + \boldsymbol g_{{\rm Ne}}\right)-\nu \left(\frac{g_{{\rm Ne}}}{a_0}\right)\boldsymbol g_{{\rm Ne}}\,. \end{equation} The 1D version of this formula has been shown to be a good approximation of the true 3D solution from a numerical Poisson solver for a random orientation of the external field, at least for computing the Galactic escape speed (Famaey et al. 2007; Wu et al. 2008). Further work should investigate the range of variation of the actual rotation curve compared to the one obtained in this way, for full numerical solutions of the modified Poisson equation and various orientations of the EFE. As mentioned in Famaey & McGaugh (2012), the EFE is negligible if ge g but can play a significant role when the gravitational field g ∼ ge a0. This condition is always reached at some point in the external part of the galaxies. In this case, the relation (6) shows that the EFE will induce a decrease in the internal gravitational field. In other words, the EFE can lead to a decrease of the external part of the rotation curves. We will study this effect more carefully in Section 3.

2021AandA...651A..24H__Simmons_&_Stewart_(1985)_Instance_1

The two main problems arising when dealing with P are the typically low signal-to-noise ratio of the polarisation signal coming from ERS and the non-Gaussian distribution of its noise statistics. Regarding the former, as mentioned above, the typical polarisation fractions of ERS at frequencies below ∼10 GHz are at most 10%. This means that only a few ERS are bright enough to be detected in polarisation with present-day technology. A standard procedure to avoid false detections in polarisation is to detect sources in total intensity and then to try to estimate their polarisation properties in a non-blind way3. We follow this approach in this paper. Regarding the latter problem, assuming that the Q and U noises are Gaussian-distributed, P would have a non-Gaussian Rice distribution (Rice 1945). Rician distribution has strictly non-negative support and heavy tails, which firstly biases the estimation of the polarisation of the sources and secondly disrupts the intuitive interpretation of signal-to-noise in terms of σ thresholds which is used virtually everywhere else in radio astronomy. Simmons & Stewart (1985) discussed four estimators which attempted to correct for biasing in the degree of linear polarisation in the presence of low signal-to-noise ratios. More recently, Argüeso et al. (2009) studied the problem in the context of CMB astronomy and developed two methods for the detection and estimation of ERS in polarisation data: one that applies the Neyman-Pearson lemma to the Rice distribution, the Neyman-Pearson filter (NPF), and another based on pre-filtering before fusion of Q and U to obtain P, the filtered fusion (FF) method. That work found that under typical CMB-experiment settings, the FF outperforms the NPF both in terms of computational simplicity and accuracy, especially for low fluxes. López-Caniego et al. (2009) applied the FF to the Wilkinson Microwave Anisotropy Probe (WMAP) five-year data. The same method has been used to study the polarisation of the Planck Second Catalogue of Compact Sources (PCCS2, Planck Collaboration XXVI 2016) and of the QUIJOTE experiment wide survey source catalogue (Herranz et al. 2021). Alternatively, a novel method for the estimation of the polarisation intensity and angle of compact sources in the E and B modes of polarisation based on steerable wavelets has been recently proposed by Diego-Palazuelos et al. (2021).

2018ApJ...866...20D__Thompson_et_al._2012_Instance_1

The physical processes involved in the formation of massive O-type stars and their feedback mechanisms are still under debate (Zinnecker & Yorke 2007; Tan et al. 2014). The energetics of O-type stars can affect the origin of new low-mass and massive stars (Deharveng et al. 2010). The massive stars are often surrounded by the bubbles/rings/semi-ringlike structures traced at mid-infrared (MIR) 8.0 μm (Churchwell et al. 2006, 2007) and are also associated with the extended radio continuum emission (e.g., Deharveng et al. 2010). Note that the majority of the studies related to the MIR bubbles are mainly carried out for a single H ii region or several H ii regions on scales of a few parsecs (e.g., Zinnecker & Yorke 2007; Deharveng et al. 2010; Rathborne et al. 2011; Kendrew et al. 2012; Simpson et al. 2012; Tackenberg et al. 2012; Thompson et al. 2012; Tan et al. 2014; Dewangan et al. 2015a, 2015b; Xu et al. 2016a). However, to our knowledge, in the Milky Way, there is still a limited detailed multiwavelength study of large-scale systems (>25 pc) of several MIR bubbles/H ii regions containing O-type stars, and hence the origin of such extended systems of H ii regions remains unexplored. These systems could be candidates for “mini-starburst” (such as the W43 “mini-starburst” region; Motte et al. 2003). With the availability of the radio recombination line (RRL) and continuum observations (e.g., Lockman 1989; Condon et al. 1998; Anderson & Bania 2009; Jones & Dickey 2012), the MIR survey (e.g., Benjamin et al. 2003), the dust continuum survey at 870 μm (e.g., Schuller et al. 2009; Urquhart et al. 2018), and the 13CO line survey (e.g., Jackson et al. 2006; Anderson et al. 2009), it appears that the H ii regions located toward the Galactic plane and the inner Galaxy are the promising sites to investigate the extended systems of O-type stars. Such study will enable us to understand the physical conditions in a densely clustered environment linked with the luminous giant H ii regions/massive star-forming complexes/mini-starburst candidates in the Galaxy. However, in particular, in the direction of the inner Galaxy, the investigation of an extended system of H ii regions is often restricted by the near–far kinematic distance ambiguity (e.g., Anderson & Bania 2009; Jones & Dickey 2012; Urquhart et al. 2018). In recent years, significant effort has been devoted to resolving the distance ambiguity for H ii regions in the inner Galaxy (see Urquhart et al. 2018 and references therein). In this work, we aim to observationally investigate a large-scale system/configuration of several H ii regions powered by O-type stars and the origin of such a large system.

2018MNRAS.475.4011B__Burrows_et_al._2011_Instance_1

ASASSN-14li was discovered by the All Sky Automated Search for Supernova (ASASSN; Shappee et al. 2014) on ut 2014–11–22.63 (MJD 56983.6) as a 16.5 magnitude source in the V band (Jose et al. 2014; Holoien et al. 2016; Brown et al. 2017a). The position of the source was found to be consistent with the centre of the post-starburst galaxy PGC 043234, with a measured projected separation of 0.04 arcsec. This galaxy is at redshift z = 0.0206 with a luminosity distance of 90.3 Mpc (for cosmological parameters H0 = 73 kms−1 Mpc−1, Ωmatter = 0.27, and ΩΛ = 0.73). It was established through archival X-ray observations of PGC 043234 from the ROSAT All-Sky Survey (Voges et al. 1999) that the galaxy does not contain an efficiently accreting AGN, with the count rate implying a luminosity orders of magnitude below standard active nuclei (e.g. Miller et al. 2015). A small number (currently six) of confirmed TDEs, including ASASSN-14li, have also been detected at radio wavelengths and the population may form a bi-modal distribution, consisting of more common non-relativistic ‘thermal’ events and rarer relativistic jets. Three events (Swift J1644+57; Burrows et al. 2011; Zauderer et al. 2011, Swift J2058+05; Cenko et al. 2012, Swift J1112.2; Brown et al. 2017b) have isotropic ∼5 GHz luminosities of between 1040 and 1042 erg s−1 whereas the rest (IGR J12580+0134; Irwin et al. 2015, XMMSL1 J0740-85; Alexander et al. 2017, ASASSN-14li; van Velzen et al. 2016; Alexander et al. 2016) have luminosities in the range 1037 to 1039 erg s−1 at similar frequencies. The higher power events are believed to result from observing down the axis of a relativistic jet, resulting in the energy of photons being significantly boosted. Even accounting for boosting, these relativistic events have a higher total energy output than their thermal counterparts. The origin of the radio emission from the thermal events is currently uncertain, with transient jets (van Velzen et al. 2016), non-relativistic winds (Alexander et al. 2016), and shocks driven by unbound material (Krolik et al. 2016) all feasible scenarios. ASASSN-14li is by far the best studied of the ‘thermal’ TDE category, having been observed extensively at Optical, UV, X-ray (where ASASSN-14li is unusually loud for an optically selected TDE), and radio wavelengths. The high cadence X-ray and radio observations in particular allow for the X-ray/radio coupling to be probed.

2019AandA...627A.135B__Bessell_&_Brett_(1988)_Instance_1

Optical color-magnitude and near-infrared (NIR) color-color diagrams are used to classify our variable candidates. The 2MASS JHKs data are available for 93 stars, while for the remaining two stars, photometric data are adopted from the UKIDSS Galactic Plane Survey (Lucas et al. 2008). The 2MASS photometry is transformed to the California Institute of Technology (CIT) system using the relations provided on their website3 to compare with the evolutionary models. Figure 6 represents the J − H/H − K CCD based on 2MASS data, typically used to classify the YSOs. The YSOs from Rebull et al. (2011) and Ogura et al. (2002) are overplotted in colored symbols. The sequence of dwarf and giants from Bessell & Brett (1988), and the intrinsic locus of CTT stars (Meyer et al. 1997) are also overplotted. The three parallel lines are the reddening vectors drawn from the tip of the giant branch (left), from the base of the MS branch (middle), and from the tip of the intrinsic CTTSs line (right). The extinction ratios to derive these reddening vectors are A J / H / K A V = 0.265 / 0.155 / 0.090 $ \frac{A_{J/H/K}}{A_V} = 0.265/0.155/0.090 $ , adopted from Cohen et al. (1981). In general, CTTSs with smaller NIR excess, WTTSs, and field stars (MS and giants) occupy the region between the left and middle reddening vectors. Figure 6 shows that most of the variables that are outliers in the proper motions and lie below the intrinsic CTTSs locus are the MS stars. Two variables (V173 and V177) are members based on their proper motions, but fall below the giant sequence. One of these, V173, does not have kinematic information. The CTTSs with large infrared excess are located in the region between the middle and right reddening vectors, while more moderate CTTSs with smaller infrared excess can also populate the region between left and middle reddening vectors, mixed with reddened WTTSs just above the CTT locus. Some contamination is expected depending on the reddening and IR excess, and also due to variability in single-epoch measurements.

2021MNRAS.502.2859N__Evans_&_Howarth_2008_Instance_1

It is harder to evaluate the behaviour of the young stellar population along the line of sight, since radial velocity measurements for our sample of Cepheids, needed for a thorough study, do not exist. Given this deficit, we provide only a simplified estimate using radial velocities of OBA-type stars from Evans & Howarth (2008). Since they belong to the same young population, we assume that they have a similar distance distribution and kinematics as the Cepheids. Fig. 16 shows the massive star sample in the plane of the sky. Except for the northernmost region (δ ≥ −72○), where no data exist, these stars cover a comparable area to the Cepheids (indicated as grey dots in the figure for comparison). The radial velocities show a distinct and well-known gradient across the SMC with higher velocities in the eastern part (see also fig. 5 of Evans & Howarth 2008). Such a gradient in radial velocity is also present in older (few Gyr) RGB stars (see fig. 9 of Dobbie et al. 2014). This gradient is commonly attributed to rotation of the SMC. Based on our results obtained for the Cepheids, we propose a different interpretation: this line-of-sight velocity gradient may instead be caused by the fact that the nearest parts of the galaxy, in the region of the SMC Wing, move with a higher radial velocity compared with the main body of the galaxy. Given the additional differences in tangential velocities, these outer parts might be in the process of being stripped from the SMC. Diaz & Bekki (2012) show in their simulations that tidal effects can produce a velocity gradient that is similar to that of a rotating disc. We stress again that this interpretation is based on the assumption that the Cepheid sample and the OBA-type stellar sample trace a similar three-dimensional distribution. For any conclusive answer, radial velocities of the Cepheid stars are required. Such measurements will be provided by the One Thousand and One Magellanic Fields (1001MC) survey (Cioni et al. 2019), which is a consortium survey with the forthcoming multi-object spectrograph 4MOST that will be mounted on the VISTA telescope.

2019ApJ...887....8H__Marty_et_al._2011_Instance_1

Silicon carbide is the best-characterized presolar mineral. It was identified more than 30 yr ago (Bernatowicz et al. 1987) because it is tagged with noble gases of anomalous isotopic compositions (Lewis et al. 1994). Subsequently, it was found that the major elements C and Si, and numerous minor elements contained in presolar SiC, have highly anomalous isotopic compositions as well, the fingerprints of nucleosynthetic processes in their parent stars. Based on the isotopic compositions of C, N, and Si, SiC was divided into distinct populations (Zinner 2014). This includes the mainstream grains, which account for about 80%–90% of all grains (depending on grain size), and the minor types AB (originally defined as two distinct types A and B), C, X, Y, Z, and (putative) nova grains. The mainstream grains have 12C/13C ratios between 10 and 100 (solar: 89), and the 14N/15N ratios of most of them are higher than the solar ratio of 440, the ratio measured for the solar wind (Marty et al. 2011). In a plot of δ29Si versus δ30Si, the mainstream grains lie along a straight line defined by δ29Si = 1.37 × δ30Si–20 (Zinner et al. 2007), where δxSi = [(xSi/28Si)grain/(xSi/28Si)solar−1) × 1000, and x = 29 or 30, i.e., δxSi is the per mil deviation from the solar xSi/28Si ratio. δ30Si values of mainstream grains vary between about −50‰ and +150‰. The isotopic compositions of heavy elements show the signatures of slow neutron-capture nucleosynthesis (s-process, Käppeler et al. 2011), which points toward low-mass (1.5–3 M⊙) asymptotic giant branch (AGB) stars of about solar or supersolar metallicity as parent stars (e.g., Lugaro et al. 2018, and references therein). The minor type Y and Z grains (a few % of all SiC grains, depending on grain size), which fall to the 30Si-rich side of the Si mainstream line, were also proposed to originate from low-mass AGB stars, but with metallicities lower than solar (Hoppe et al. 1997; Amari et al. 2001b). This low-metallicity scenario, however, was recently questioned (Liu et al. 2019). The type C (∼0.1% of all SiC grains) and X grains (∼1% of all SiC grains) are believed to originate from core-collapse supernovae (CCSNe; Amari et al. 1992; Hoppe et al. 1996b; Nittler et al. 1996; Gyngard et al. 2010). These grains show strong depletions (X grains) or enrichments (C grains) in the heavy Si isotopes. Their 12C/13C ratios span a large range from 10 to >10,000; other characteristic features of X and C grains are enrichments in 15N and high initial 26Al/27Al ratios of typically >0.1. Putative nova grains (∼0.1% of all SiC grains) have low 12C/13C ratios of 10, low 14N/15N ratios of 40, and high initial 26Al/27Al ratios of up to 0.2 (Amari et al. 2001a); their origins in the outflows of nova explosions, however, were questioned and SNe were proposed for at least some of the putative nova grains instead (Nittler & Hoppe 2005; Pignatari et al. 2015; Liu et al. 2017a; Hoppe et al. 2018b).

2015AandA...579A.102B__Boselli_et_al._2009_Instance_1

Once corrected for dust attenuation, Hα luminosities can be transformed into star formation rates (SFR, in M⊙ yr-1) using a factor that depends on the assumed IMF and stellar model7: (10)\begin{equation} {SFR = k({\rm H}\alpha) \times L({\rm H}\alpha)_{\rm cor}} . \end{equation}SFR=k(Hα)×L(Hα)cor.We recall that this relation is valid only under the assumption that the mean star formation activity of the emitting galaxies is constant on a timescale of a few Myr, roughly comparable to the typical time spent by the stellar population responsible for the ionisation of the gas on the main sequence (Boselli et al. 2009; Boissier 2013; Boquien et al. 2014). The ionising stars are O and early-B stars, whose typical age is ≲107 yr. The stationarity condition is generally satisfied in massive, normal, star-forming galaxies undergoing secular evolution. In these objects, the total number of OB associations is significantly larger than the number of HII regions under formation and of OB stars reaching the final stage of their evolution, thus their total Hα luminosity is fairly constant with time. This might not be the case in strongly perturbed systems or in dwarf galaxies, where the total star formation activity can be dominated by individual giant HII regions (Boselli et al. 2009; Weisz et al. 2012), and the IMF is only stochastically sampled (Lee et al. 2009; Fumagalli et al. 2011; da Silva et al. 2014). The HRS sample is dominated by relatively massive galaxies undergoing secular evolution. For these objects, Eq. (10) can thus be applied. The sample, however, also includes galaxies in the Virgo cluster region, where the perturbation induced by the cluster environment might have affected their star formation rate (e.g. Boselli & Gavazzi 2006, 2014). Models and simulations have shown that in these objects the suppression of star formation occurs on timescales of a few hundred Myr (Boselli et al. 2006, 2008a,b, 2014d). These timescales are relatively long compared to the typical age of O-B stars. The recent work of Boquien et al. (2014) has clearly shown that the Lyman continuum emission tightly follows the rapid variations in the star formation activity of simulated galaxies down to timescales of a few Myrs. We can thus safely consider that the linear relation between the Hα luminosity and the star formation rate given in Eq. (10) is satisfied in the HRS sample.

2021AandA...656A..16C__Bruno_&_Carbone_2013_Instance_3

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).

2022MNRAS.514.3894O__Buxton_et_al._2012_Instance_1

The GBHT outburst light curves could be very complicated, and while the so-called ‘the main outburst’ could go through the spectral states described above, some GBHTs also show rebrightening episodes during the outburst decay (Kalemci et al. 2013) and/or an increase in brightness several days after the X-ray flux goes below the detection limits of the most observatories that are sometimes defined as mini-outbursts (Chen et al. 1997). A systematic multiwavelength study of GBHTs in the outburst decay by Kalemci et al. (2013) showed that for most of the systems, a rebrightening (secondary maximum or secondary flare) in OIR occurred ∼1–2 weeks after the soft-to-hard transition. Detection of rebrightening during the outburst decay supports the argument that the formation of a compact jet and its interaction with the accretion environment are imprinted on the multiwavelength behaviour of the GBHTs (Buxton & Bailyn 2004; Kalemci et al. 2005, 2013; Buxton et al. 2012; Dinçer et al. 2012; Corbel et al. 2013). Alternatively, the synchrotron radiation from the hot accretion flow model (Poutanen 1998; Veledina et al. 2013), or the irradiation from the secondary star or outer part of the disc could explain the brightness increase in the OIR bands. In contrast, there are limited number of pointed hard X-ray observations for the mini-outbursts (e.g. XTE J1752 − 223, SWIFT J1745 − 26, and V404 Cyg, Chun et al. 2013; Kalemci et al. 2014; Muñoz-Darias et al. 2017) since they have been observed frequently in the soft X-rays and optical (see Chen et al. 1997 for some historical examples, both in black holes and neutron stars). A recent study by Zhang et al. (2019) attempted a classification of the rebrightenings during/after the main outburst decay based on the the available fluxes and applied this scheme to Swift J1753.5 − 0127, which showed a mini-outburst in radio, optical, and X-rays. It can be seen that different flavours exist depending on whether the source reaches quiescence first. Some sources show multiple mini-outbursts after the initial outburst (e.g. XTE J1650 − 500, MAXI J1535 − 571, Tomsick et al. 2003; Cúneo et al. 2020). Although the origin of the mini-outbursts is still debated, an increased mass accretion triggered by the events during the evolution of the primary outburst through heating of the outer parts of the accretion disc (Ertan & Alpar 2002), or the companion star (Augusteijn et al. 1993), is known to be the likely explanations.

2021ApJ...923L..22A__Cordes_&_Jenet_2012_Instance_1

Pulsar timing experiments (Sazhin 1978; Detweiler 1979) allow us to explore the low-frequency (∼1–100 nHz) part of the gravitational-wave (GW) spectrum. By measuring deviations from the expected arrival times of radio pulses from an array of millisecond pulsars, we can search for a variety of GW signals and their sources. The most promising sources in the nanohertz part of the GW spectrum are supermassive binary black holes (SMBHBs) that form via the mergers of massive galaxies. Orbiting SMBHBs produce a stochastic GW background (GWB; Lommen & Backer 2001; Jaffe & Backer 2003; Volonteri et al. 2003; Wyithe & Loeb 2003; Enoki et al. 2004; Sesana et al. 2008; McWilliams et al. 2012; Sesana 2013; Ravi et al. 2015; Rosado et al. 2015; Kelley et al. 2016; Sesana et al. 2016; Dvorkin & Barausse 2017; Kelley et al. 2017; Bonetti et al. 2018; Ryu et al. 2018), individual periodic signals or continuous waves (CWs; Sesana et al. 2009; Sesana & Vecchio 2010; Mingarelli et al. 2012; Roedig & Sesana 2012; Ravi et al. 2012, 2015; Rosado et al. 2015; Schutz & Ma 2016; Mingarelli et al. 2017; Kelley et al. 2018), and transient GW bursts (van Haasteren & Levin 2010; Cordes & Jenet 2012; Ravi et al. 2015; Madison et al. 2017; Islo et al. 2019; Bécsy & Cornish 2021). We expect to detect the GWB first, followed by detection of individual SMBHBs (Siemens et al. 2013; Rosado et al. 2015; Taylor et al. 2016; Mingarelli et al. 2017) that stand out above the GWB. Detection of GWs from SMBHBs will yield insights into galaxy mergers and evolution not possible through any other means. Other potential sources in the nanohertz band include cosmic strings (Damour & Vilenkin 2000, 2001; Berezinsky et al. 2004; Damour & Vilenkin 2005; Siemens et al. 2006, 2007; Ölmez et al. 2010; Sanidas et al. 2013; Blanco-Pillado et al. 2018; Chang & Cui 2021; Ghayour et al. 2021; Gorghetto et al. 2021; Wu et al. 2021a; Blanco-Pillado et al. 2021; Lin 2021; Chiang & Lu 2021; Lazarides et al. 2021; Chakrabortty et al. 2021; Ellis & Lewicki 2021), phase transitions in the early universe (Witten 1984; Caprini et al. 2010; Addazi et al. 2021; Arzoumanian et al. 2021; Di Bari et al.2021; Borah et al. 2021; Nakai et al. 2021; Brandenburg et al.2021; Neronov et al. 2021), and relic GWs from inflation (Starobinskiǐ 1979; Allen 1988; Lazarides et al. 2021; Ashoorioon et al. 2021; Yi & Zhu 2021; Li et al. 2021; Poletti 2021; Vagnozzi 2021; Sharma 2021), all of which would provide unique insights into high-energy and early-universe physics.

2016MNRAS.461.4176H__Jaffe_&_Kaiser_1995_Instance_1

The Bayesian approach is indeed robust and optimal, within the context mentioned above. It focuses on the reconstruction of the LSS within the framework of the standard model of cosmology and for a given data base. However, most previous studies have not addressed the question how consistent is the assumed cosmological model with the observed data. This is not a trivial issue – the model needs to agree with the data so as to provide a solid foundation for the WF/CRs construction. Ideally, one should have started with establishing the agreement of the model with the data and only then reconstruct the LSS in the manner described above. However, history does not always proceeds in a linear fashion. The aim of the paper is to amend that situation and establish the likelihood of peculiar velocities data bases given the standard model of cosmology. The relevant methodology is straightforward and well established. One needs to calculate the likelihood function of the data given the model – namely the probability of the occurrence of the data within the framework of the assumed model (Jaffe & Kaiser 1995; Zaroubi et al. 1995; Hoffman 2001; Press et al. 2007). The likelihood function establishes the goodness-of-fit (GoF) of the data by the model. In the cosmological case and under the assumption of the linear regime, where the velocity field constitutes a random Gaussian vector field and the observational errors are normally distributed, the likelihood analysis amounts to calculating a χ2 statistics. This approach was indeed applied to velocity data bases (Jaffe & Kaiser 1995; Zaroubi et al. 1997, 2001). The application of the likelihood analysis to actual velocity data bases suffers however from one major drawback. The gravitational dynamics of structure formation induces non-linear contributions to the velocities of galaxies. These non-linear corrections render the parameter estimation and GoF analysis to be rather uncertain. The remedy to the problem involves the filtering of small scales to give linearized data. The likelihood analysis can then be safely applied to the linearized data. Here, we suggest such a small-scales filtering procedure and study the extent by which Cosmicflows-2 (CF2; Tully et al. 2013) is compatible with the Λ cold dark matter (ΛCDM) standard model of cosmology.

2018MNRAS.480.4154C__Maisinger,_Hobson_&_Lasenby_2004_Instance_1

Classical imaging techniques were developed in the field to solve the RI reconstruction problem, such as clean and its multiscale variants (Högbom 1974; Bhatnagar & Corwnell 2004; Cornwell 2008; Stewart, Fenech & Muxlow 2011). In particular, clean builds a model image by iteratively removing point source components from the residuals of the acquired data (at each iteration). clean-based algorithms, however, are typically slow (generally requiring computationally demanding major cycles; cf. Clark clean), requiring fine-tuning and supervision, while providing suboptimal imaging quality (see e.g. Li, Cornwell & de Hoog 2011a; Carrillo, McEwen & Wiaux 2012). Another classical technique is the maximum entropy method (MEM) (Ables 1974; Gull & Daniell 1978), extended to RI imaging by Cornwell & Evans (1985). The MEM approach of Cornwell & Evans (1985) developed for RI imaging considers a regularization problem consisting of a relative entropic prior, a (Gaussian) likelihood term and an additional flux constraint. In principle, MEM requires less fine-tuning and supervision compared to clean and can therefore alleviate part of the shortcomings of clean-based algorithms. However, an optimal metric – expressed as an entropy functional – is not known in advance and therefore needs to be chosen individually (Starck et al. 2001; Maisinger, Hobson & Lasenby 2004). Indeed, it is widely known that MEM fails to reconstruct sharp and smooth image features simultaneously. Recently, the theory of compressed sensing (CS) has suggested the use of sparse representation and regularization approaches for the recovery of sparse signals from incomplete linear measurements (Donoho 2006; Candes & Wakin 2008; Candes et al. 2010), which has shown great success. CS techniques based on sparse regularization were ushered into RI imaging for image reconstruction (Suksmono 2009; Wiaux et al. 2009a,b; Wenger et al. 2010; Li et al. 2011a,b; McEwen & Wiaux 2011; Carrillo et al. 2012; Wolz et al. 2013; Carrillo, McEwen & Wiaux 2014; Dabbech et al. 2015; Garsden et al. 2015; Onose et al. 2016; Dabbech et al. 2017; Kartik et al. 2017; Onose, Dabbech & Wiaux 2017; Pratley et al. 2018) and have shown promising results and improvements compared to traditional approaches such as clean-based methods and MEM. In general, such approaches can recover sharp and smooth image features simultaneously (e.g. Carrillo et al. 2012). While sparse approaches have been shown to be highly effective, the best approach to image different sources remains an open question. Algorithms have been developed to scale sparse approaches to big-data (Carrillo et al. 2014; Onose et al. 2016; Cai, Pratley & McEwen 2017a; Kartik et al. 2017; Onose et al. 2017), such as that anticipated from the Square Kilometre Array (SKA1). However, clean-based methods, MEM, and CS-based methods, unfortunately, do not provide any uncertainty quantification about the accuracy of recovered images.

2020AandA...641A.155V__Spilker_et_al._(2016)_Instance_1

The right panel of Fig. 7 shows the relation between ΣSFR and R52. For each object, we computed ΣSFR = SFR/(2πR2), where R is a representative value of the galaxy radius. The latter is rather arbitrary and it depends on the chosen tracer, the depth, resolution, and wavelength of the observations. Here we adopted the ALMA sizes from circular Gaussian fitting for our sample, assuming R = FWHM/2. As mentioned in Sect. 3.2, this estimate combines all the available lines and continuum measurements, resulting in a size representative of the dust and gas content of each galaxy (Puglisi et al. 2019). We further recomputed the ΣSFR for the BzK galaxies in D15, using the Gaussian best-fit results of the rest-frame UV observations to be consistent with our estimates. For the SPT-SMGs, we used the sizes of Spilker et al. (2016), while we employed the 1.4 GHz radio measurements in Liu et al. (2015b) for the local spirals. For reference, we also show the mean values for the BzK galaxies, the local spirals, and ULIRGs as in D15. The best-fit model to the observed points returns a 60% flatter slope than in D15 (Table 3), but the trends are qualitatively similar. We restate that the choice of the tracer, the resolution, and depth of the observations play a major role in setting the exact values of the slope and intercept in our simple linear model, which should be thus taken with a grain of salt. This is particularly true for spatially resolved local objects, where we attempted to replicate the global, galaxy-scale measurements that can be obtained for distant objects. The observed data points in Fig. 7 qualitatively agree with the simulations by Narayanan & Krumholz (2014) and Bournaud et al. (2015), and they support the validity of ΣSFR as a good proxy for the gas conditions in galaxies. The total SFR is a worse predictor of the gas excitation conditions (Lu et al. 2014; Kamenetzky et al. 2016), since it does not correlate with the density and temperature probability distribution functions in clouds (Narayanan & Krumholz 2014). Interestingly, this seems to be partially confirmed by the linear regression analysis we applied here (Table 3): when modeling R52 as a function of LIR (∝SFR, Fig. 5) and ΣSFR, we do find similar slopes, but also a larger correlation coefficient ρ for ΣSFR than for LIR. However, LIR alone does correlate with the CO line luminosity ratio.

2022MNRAS.512.3137Z__Katz_et_al._1999_Instance_2

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).

2020ApJ...900..100R__White_et_al._2019_Instance_1

It is much harder to localize and track the formation of current sheets in realistic black hole accretion flows in a larger domain and for a longer period because of the effects of the more complicated global dynamics governed by the central object, and due to the turbulence induced by the MRI. Both the evolution of accretion flows and the formation of current sheets therein strongly depend on the magnetic field geometry. We model an accretion disk around a rotating black hole, varying the initial conditions to study current sheet formation in different scenarios of magnetic field geometry. In the magnetically arrested disk (MAD; Igumenshchev et al. 2003; Narayan et al. 2003) scenario, the MRI and subsequent turbulence in the inner accretion disk are suppressed due to large-scale magnetic flux (see, e.g., White et al. 2019). In axisymmetric simulations as considered here, the arrested inflow is regularly broken by frequent bursts of accretion, allowing for a macroscopic equatorial current sheet to form and break in a periodic fashion. In a full 3D setup, magnetically buoyant structures are interchanged with less-magnetized dense fluid (Igumenshchev 2008; White et al. 2019), resulting in a magnetic Rayleigh–Taylor instability (Kruskal & Schwarzschild 1954) potentially sourcing interchange-type magnetic reconnection. In the Standard And Normal Evolution (SANE; Narayan et al. 2012; Sadowski et al. 2013) state, a fully turbulent accretion disk can develop due to a smaller magnetic flux (see, e.g., Porth et al. 2019), and current sheets can ubiquitously form and interact with the turbulent flow. Polarized synchrotron radiation observed by the Event Horizon Telescope (Event Horizon Telescope Collaboration et al. 2019a) can probe the field-line structure at event-horizon scales and put tighter constraints on the magnetization and address whether the accretion is in a SANE or a MAD state (Event Horizon Telescope Collaboration et al. 2019b).

2020ApJ...893..124Z__Tenbarge_&_Howes_2013_Instance_1

In hydrodynamic and magnetohydrodynamic systems, different fluctuations interact with each other nonlinearly, generating turbulence (Matthaeus et al. 2015). One of the most important characteristics of turbulence is the existence of intermittency among various scales. Spatial intermittency manifests as coherent structures with large gradient at small scales. In ordinary fluids, coherent structures include a tangle of vortex filaments where vorticity is highly concentrated (Frisch 1995). In the turbulent plasma environments, examples of intermittent structures include current sheets, discontinuities, shock waves, and Alfvénic vortices (Veltri & Mangeney 1999; Sundkvist et al. 2007; Lion et al. 2016; Wang et al. 2019). The intermittency will influence the measured statistical properties of the fluctuating quantities. For example, the growth of flatness with decreasing scales indicates the existence of intermittency. Plenty of simulation works (Servidio et al. 2011; Karimabadi et al. 2013; Tenbarge & Howes 2013; Wan et al. 2015, 2016; Zhang et al. 2015) have indicated that dissipation, acceleration, and thermalization of turbulent plasmas mainly take place near intermittent structures on kinetic scale, while the actual physical mechanisms behind dissipation remain unclear. There are a variety of diagnostic approaches to measure intermittency. (1) Probability density functions (PDFs) of scale-dependent field increments develop heavy tails because of intermittency, and the tail is more enhanced with increasing intermittency (Marsch & Tu 1994). (2) The scale dependency of the normalized fourth-order moment, known as flatness, , where δv = v(t + τ)−v(t), is an alternative representation. This quantity increases as the intermittency becomes more significant along with decreasing scales. (3) The p-th order structure function, , will be larger than that for Gaussian PDF without intermittency and the scaling exponent, ζ(p), has a nonlinear form when intermittency presents. Based on such diagnostic approaches, the properties of intermittency in plasmas have been widely investigated in the solar atmosphere, magnetosheath, solar wind, termination shock, etc. (Burlaga 1991a, 1991b; Marsch & Liu 1993; Horbury et al. 1995; Macek et al. 2011, 2017; Chasapis et al. 2018). These observational analyses have revealed that intermittency is widely existing in the heliosphere and immensely influencing plasma dynamics.

2018MNRAS.475.4011B__Zauderer_et_al._2011_Instance_1

ASASSN-14li was discovered by the All Sky Automated Search for Supernova (ASASSN; Shappee et al. 2014) on ut 2014–11–22.63 (MJD 56983.6) as a 16.5 magnitude source in the V band (Jose et al. 2014; Holoien et al. 2016; Brown et al. 2017a). The position of the source was found to be consistent with the centre of the post-starburst galaxy PGC 043234, with a measured projected separation of 0.04 arcsec. This galaxy is at redshift z = 0.0206 with a luminosity distance of 90.3 Mpc (for cosmological parameters H0 = 73 kms−1 Mpc−1, Ωmatter = 0.27, and ΩΛ = 0.73). It was established through archival X-ray observations of PGC 043234 from the ROSAT All-Sky Survey (Voges et al. 1999) that the galaxy does not contain an efficiently accreting AGN, with the count rate implying a luminosity orders of magnitude below standard active nuclei (e.g. Miller et al. 2015). A small number (currently six) of confirmed TDEs, including ASASSN-14li, have also been detected at radio wavelengths and the population may form a bi-modal distribution, consisting of more common non-relativistic ‘thermal’ events and rarer relativistic jets. Three events (Swift J1644+57; Burrows et al. 2011; Zauderer et al. 2011, Swift J2058+05; Cenko et al. 2012, Swift J1112.2; Brown et al. 2017b) have isotropic ∼5 GHz luminosities of between 1040 and 1042 erg s−1 whereas the rest (IGR J12580+0134; Irwin et al. 2015, XMMSL1 J0740-85; Alexander et al. 2017, ASASSN-14li; van Velzen et al. 2016; Alexander et al. 2016) have luminosities in the range 1037 to 1039 erg s−1 at similar frequencies. The higher power events are believed to result from observing down the axis of a relativistic jet, resulting in the energy of photons being significantly boosted. Even accounting for boosting, these relativistic events have a higher total energy output than their thermal counterparts. The origin of the radio emission from the thermal events is currently uncertain, with transient jets (van Velzen et al. 2016), non-relativistic winds (Alexander et al. 2016), and shocks driven by unbound material (Krolik et al. 2016) all feasible scenarios. ASASSN-14li is by far the best studied of the ‘thermal’ TDE category, having been observed extensively at Optical, UV, X-ray (where ASASSN-14li is unusually loud for an optically selected TDE), and radio wavelengths. The high cadence X-ray and radio observations in particular allow for the X-ray/radio coupling to be probed.

2022ApJ...933..243F__Woosley_&_Bloom_2006b_Instance_1

Gamma-ray bursts (GRBs) are among the most powerful gamma-ray sources in the universe. They could be generated from the merger of binary compact objects (BCOs; Duncan & Thompson 1992; Usov 1992; Thompson 1994; Metzger et al. 2011) or the death of massive stars (Woosley 1993; Paczyński 1998; Woosley & Bloom 2006a). The merger of BCOs; a black hole (BH)–a neutron star (NS) or NS–NS, leading to kilonovae (KNe), is correlated with short-duration gamma-ray bursts (sGRBs; T 90 10 10 T 90 is defined as the time during which the cumulative number of collected counts above background rises from 5% to 95%. ≲ 2 s; Li & Paczyński 1998; Rosswog 2005; Metzger et al. 2010; Kasen et al. 2013; Metzger 2017). On the other hand, long-duration gamma-ray bursts (lGRBs; T 90 ≳ 2 s; Kouveliotou et al. 1993) are associated with the core collapse (CC) of dying massive stars (Woosley 1993; Galama et al. 1998) leading to supernovae (SNe; Bloom et al. 1999; Woosley & Bloom 2006b). It is believed that in both scenarios large quantities of materials with a wide range of velocities are ejected. In the framework of CC-SNe (depending on the type of SN association), several materials ejected with sub-relativistic velocities less than β ≲ 0.4 11 11 Hereafter, we adopt natural units c = ℏ = 1. have been reported (see, e.g., Kulkarni et al. 1998; Bloom et al. 1999; Woosley & Bloom 2006b; Valenti et al. 2008; Gal-Yam 2017; Izzo et al. 2019, 2020; Modjaz et al. 2020; Nicholl et al. 2020). Regarding the merger of two NSs, sub-relativistic materials such as the cocoon, the shock breakout, and the dynamical and wind ejecta are launched with velocities in the range 0.03 ≲ β ≲ 0.8 12 12 Some authors have considered the shock breakout material in the sub-, trans-, and ultra-relativistic regimes (see, e.g., Kyutoku et al. 2014; Metzger et al. 2015; Fraija et al. 2019c). (see, e.g., Dessart et al. 2009; Metzger & Fernández 2014; Fernández et al. 2015; Kyutoku et al. 2014; Metzger et al. 2015; Nagakura et al. 2014; Murguia-Berthier et al. 2014; Lazzati et al. 2017, 2018; Goriely et al. 2011; Hotokezaka et al. 2013; Bauswein et al. 2013; Wanajo et al. 2014). While the mass and velocity inferred for the first GRB/KN association 13 13 GRB 170817A/AT 2017gfo. were M ej ≈ (10−4−10−2)M ⊙ and β ≈ (0.1−0.3), respectively (Coulter et al. 2017; Arcavi et al. 2017; Cowperthwaite et al. 2017; Nicholl et al. 2017; Metzger 2019), the mass and velocity inferred for the first GRB/SN association 14 14 GRB 980425/SN1998bw. was M ej ≈ 10−5 M ⊙ and β ≈ (0.2–0.3), respectively (Kulkarni et al. 1998).

2017ApJ...835....2X__Collins_et_al._2012_Instance_1

On the other hand, a clear physical interpretation of the observed pulse broadening phenomenon requires a good understanding of the interstellar electron density structure. A power-law model of electron density fluctuations is commonly adopted in theoretical constructions on radio wave propagation (Lee & Jokipii 1976; Rickett 1977, 1990) and is compatible with observational indications (e.g., Armstrong et al. 1995). Recent advances in understanding the properties of magnetohydrodynamic (MHD) turbulence (Goldreich & Sridhar 1995; Lithwick & Goldreich 2001; Cho & Lazarian 2002, 2003) stimulate a renewed investigation on density statistics (Beresnyak et al. 2005; Kowal et al. 2007; Lazarian et al. 2008; Burkhart et al. 2009, 2010, 2015; Collins et al. 2012; Federrath & Klessen 2012), which provide important insight into key physical processes such as star formation in the turbulent and magnetized ISM (see reviews by, e.g., McKee & Ostriker 2007; Lazarian et al. 2015). The density spectrum in compressible astrophysical fluids was systematically studied in Kowal et al. (2007) by carrying out an extensive set of MHD numerical simulations with varying compressibility and magnetization. Instead of a single Kolmogorov slope with a power-law index of , significant variations in the spectral slope of density fluctuations are present. For supersonic turbulence, their results are consistent with earlier findings in both magnetized (Beresnyak et al. 2005) and nonmagnetized (Kim & Ryu 2005) fluids. It shows that the density power spectrum becomes shallower as the sonic Mach number ( ) increases, where VL is the turbulent velocity at the outer scale of turbulence and cs is the sound speed in the medium, and there is a significant excess of density structures at small scales in highly supersonic turbulence. This behavior is naturally expected as the gas is compressed in shocks by supersonic flows and the interacting shocks produce local density enhancements (Mac Low & Klessen 2004; Padoan et al. 2004b).

2017MNRAS.471.2917K__Glenn_et_al._2015_Instance_1

The only way to access the transitions which trace warmer molecular gas is to get above the atmosphere as did the Herschel Space Observatory The Herschel SPIRE Fourier Transform Spectrometer (FTS) was simultaneously sensitive to the J = 4−3 (EJ = 4 = 55 K) up to the J = 13−12 (EJ = 13 = 503 K) line of CO for local galaxies. With this access to higher energy CO transitions, Herschel gave us the tools to more finely discern the lower and higher excitation components of the multiphase molecular ISM. To more finely measure the excitation of the molecular ISM is to better understand energy exchange between the star formation and the ISM. Many studies of individual galaxies (Panuzzo et al. 2010; Rangwala et al. 2011; Hailey-Dunsheath et al. 2012; Kamenetzky et al. 2012; Spinoglio et al. 2012; Meijerink et al. 2013; Pellegrini et al. 2013; Rigopoulou et al. 2013; Israel et al. 2014; Papadopoulos et al. 2014; Rosenberg et al. 2014; Schirm et al. 2014; Glenn et al. 2015; Wu et al. 2015) and surveys (Papadopoulos et al. 2010, 2012a,b; Pereira-Santaella et al. 2013, 2014; Greve et al. 2014; Kamenetzky et al. 2014, 2016; Lu et al. 2014; Daddi et al. 2015; Israel, Rosenberg & van der Werf 2015; Liu et al. 2015; Mashian et al. 2015) have shown that the higher J lines of CO arise from warmer, denser gas than the cold gas responsible for the emission of for example CO J = 1−0. Though this warm gas is only a small fraction (∼10 per cent) of the total molecular mass, it is responsible for ∼90 per cent of the CO luminosity (Kamenetzky et al. 2014). This high CO luminosity cannot be explained by excitation from the ultraviolet (UV) light from young O and B stars in photon-dominated regions (PDRs); mechanical heating is often required. Theorists have attempted to use hydrodynamical galaxy simulations to produce galaxy-integrated spectral line energy distributions (SLEDs) of CO emission (Narayanan et al. 2008; Olsen et al. 2016), but we still lack a comprehensive picture of the mechanisms responsible for this emission. Furthermore, even higher J lines of CO than addressed here (above J = 13−12) have been detected, for example with Herschel PACS, indicating a third, even higher temperature component of molecular gas (NGC1068, Hailey-Dunsheath et al. 2012). The CO SLEDs from J = 14−13 through J = 30−29 show a large range in SLED shape, even among similar galaxies (Mashian et al. 2015).

2022MNRAS.510.1043B__Kukula_et_al._1998_Instance_1

The origin of the radio emission in radio-loud active galactic nuclei (RL AGN) is clear, luminous relativistic jets of magnetized plasma, which can extend far out, on the host galaxy scale and beyond. Conversely, radio-quiet (RQ) AGN are associated with radio emission which is typically 103 times weaker (as defined by Kellermann et al. 1989), in smaller structures (kpc-pc scale; e.g. Blundell et al. 1996; Nagar et al. 1999; Ulvestad et al. 2005a; Gallimore et al. 2006) with sub-relativistic velocities (e.g. Middelberg et al. 2004; Ulvestad, Antonucci & Barvainis 2005b) compared to RL AGN. The reduced sizes and low brightness of the radio emission of RQ AGN create a major challenge for detailed studies, in sharp contrast with the thoroughly studied RL AGN. The fewer radio studies of RQ AGN (e.g. Barvainis & Antonucci 1989; Kellermann et al. 1994; Barvainis, Lonsdale & Antonucci 1996; Kukula et al. 1998; Ulvestad et al. 2005a; Leipski et al. 2006; Doi et al. 2011; Padovani et al. 2011; Zakamska et al. 2016; Jarvis et al. 2019, 2021; Fawcett et al. 2020; Nyland et al. 2020; Smith et al. 2020b; Baldi et al. 2021a) generally lead to mixed results. This encourages to keep investigating it, as it indicates that the origin of the radio emission in RQ AGN is still an open question. If a number of different processes are indeed involved, then the radio band can be used to probe a range of physical processes, rather than being heavily dominated by a single process, as occurs in RL AGN (see Blandford, Meier & Readhead 2019 Panessa et al. 2019 for reviews). From large scale to small scale: (i) host galaxy star formation (SF) could account for the observed FIR-to-radio emission observed in active and non-active galaxies (Condon et al. 2013; Zakamska et al. 2016); (ii) an AGN-driven wind is expected to shock the interstellar gas, leading to particle acceleration and radio synchrotron emission, which may reach the observed flux level (Jiang et al. 2010); (iii) the intense radiation of the AGN photoionizes large volumes of ambient gas, as supported by the strength of the narrow and broad line emission observed in type-1 AGN, leading to thermal free–free emission in the radio band (Baskin & Laor 2021); (iv) a scaled-down jet, physically similar to the one in RL AGN, but much fainter, less energetic and slower (Barvainis et al. 1996; Gallimore et al. 2006; Talbot, Sijacki & Bourne 2021); (v) a tight radio/X-ray luminosity relation for AGN (∼10−5; Laor & Behar 2008) similar to coronally active stars (Güdel & Benz 1993; Güdel et al. 2002) suggests that coronal emission from magnetic activity above the accretion disc (Field & Rogers 1993; Gallimore, Baum & O’Dea 1997) may produce the observed radio emission.

2019ApJ...882..144K__Berk_et_al._2001_Instance_1

The FOCAS and NIRSPEC spectra of PSO J006+39 were obtained at two different epochs separated by 1 yr and 9 months (by slightly less than 3 months in the quasar rest frame). Previously, we found that the PS1 y-band light curve of PSO J006+39 shows brightness variations with a peak-to-peak amplitude of ∼0.7 mag over ∼4 yr (Koptelova et al. 2017), which might be due to the flux variations of both continuum and Lyα line of PSO J006+39. To infer the brightness state of PSO J006+39 at the epochs of its FOCAS and NIRSPEC observations, we first calculated the spectral slope of the quasar continuum from the NIRSPEC spectrum with a wider wavelength coverage than that of the FOCAS spectrum. Using wavelength intervals of 11100–11300, 11400–11600, 13085–13400, and 14700–15200 Å we measured a spectral slope of αλ = −1.35 ± 0.26, where the quoted uncertainty is the statistical error of the fit. The fitted power law is shown in Figure 2 with a solid line. The estimated continuum slope is consistent but somewhat flatter than the typical slope of luminous quasars (Zheng & Malkan 1993; Vanden Berk et al. 2001; Selsing et al. 2016). We then fitted the FOCAS data using the power law with a fixed spectral slope of αλ = −1.35 and spectral windows of 9700–9850 and 10050–10100 Å. The spectral windows adopted for the analysis of the FOCAS and NIRSPEC spectra were taken to be similar to the rest-frame wavelength intervals commonly used to fit the continua of quasars (Vanden Berk et al. 2001; Decarli et al. 2010; Lusso et al. 2015) and less affected by the contribution from emission lines on the red side of the Big Blue Bump (BBB; e.g., Malkan 1983). The estimated continuum flux of PSO J006+39 at the epoch of its FOCAS observations is shown in Figure 2 with a dashed line. By comparing the continuum flux at the epochs of the FOCAS and NIRSPEC observations, we find that the brightness state of PSO J006+39 was different at these two epochs. PSO J006+39 was brighter by about 0.8 mag during the FOCAS observations than during the NIRSPEC observations. Thus, the continuum flux of PSO J006+39 might be different at different epochs depending on the brightness state of the quasar. Figure 2 also shows the fluxes of PSO J006+39 in the FOCAS Y, and NIRSPEC N2, N4, and N6 bands at the epochs of the FOCAS and NIRSPEC observations (see also Table 1).

2020AandA...640A.121G__Swain_et_al._2009_Instance_1

The planetary physical properties that can be derived from measurements with the two most successful exoplanet detection techniques, that is, with the radial velocity method and the transit method, are the planet radius (if the planet transits its star), mass (a lower limit if the planet does not transit its star), and the orbital period and distance, eccentricity, and inclination (if the planet transits its star) (see Perryman 2018, and references therein). The upper atmospheres (above optically thick clouds) of giant exoplanets and those of smaller planets around M stars can be probed using spectroscopy during primary and/or secondary transits (Bean et al. 2010; Swain et al. 2009, 2008; Tinetti et al. 2007; Nakajima 1983). It appears to be virtually impossible to characterize the (lower) atmospheresand surfaces of small, Earth-like planets in the habitable zones of solar-type stars (Bétrémieux & Kaltenegger 2014; Misra et al. 2014; Kaltenegger & Traub 2009), in particular because the light of the parent star is refracted while traveling through the lower atmosphere of its planet and emerges forever out of reach of terrestrial telescopes (García Muñoz et al. 2012). The (lower) atmosphere and surface of a planet are crucial for determining the habitability of a planet, as they hold information about cloud composition, trace gases in disequilibrium and probably most importantly, liquid surface water (see, e.g., Schwieterman et al. 2018; Kiang et al. 2007a,b, and references therein). For such a characterization of terrestrial-type planets, direct observations of the thermal radiation that they emit or of the light of their parent star that they reflect are required. The numerical results that we present in this paper concern the reflected starlight. Because of the huge distances involved, any measured reflected starlight pertains to the (illuminated and visible part of the) planetary disk. It therefore is a disk-integrated signal.

2020ApJ...900...45L__Helmi_et_al._2018_Instance_1

Much of the recent work on this topic has focused on gathering observational evidence for hierarchical growth through the identification of stellar streams and tidal interactions, which are thought to be the hallmark of low-mass galaxies having been accreted by more massive galaxies. The Sagittarius dwarf galaxy (Ibata et al. 1994) and the stellar streams associated with it (Belokurov et al. 2006) are some of the most well-studied examples of this type of hierarchical accretion in the Milky Way. Recent data from the Gaia mission (Gaia Collaboration et al. 2016) and other large-scale surveys, as well as analyses that combine kinematic data with information about chemical abundances and/or specific stellar populations, have led to discoveries of additional substructures in the Galaxy and brought renewed attention to this topic (e.g., Belokurov et al. 2018; Deason et al. 2018; Helmi et al. 2018; Myeong et al. 2018). The Andromeda galaxy (M31) has been studied in detail as well. Structures formed from spatial overdensities of the red giant branch (RGB) population, e.g., the Giant Stellar Stream, have been found in the outer halo of M31 by using resolved RGB star maps (Ibata et al. 2001; Ferguson et al. 2002). Using the surface density of resolved RGB stars, it is possible to infer the surface brightness distribution of the underlying light; structures revealed by this technique, such as faint streams and clumps of RGB stars, suggest a rich history of accretion events and tidal interactions for M31. Deep observations of galaxies outside the Local Group have also revealed streams, shells, and satellites using resolved RGB stars and unresolved low surface brightness (LSB) features (e.g., Janowiecki et al. 2010; Crnojevic et al. 2016; Mihos et al. 2017). However, in galaxies well beyond the Local Group, RGB stars are unresolved and a different tracer of hierarchical assembly processes and tidal interactions is required. Globular clusters (GCs) have a number of properties that make them well suited for this task.

2015ApJ...806..167G__Daughton_&_Karimabadi_2007_Instance_1

We envision a situation where intense current sheets are developed within a magnetically dominated plasma. Earlier work in nonrelativistic low-β plasmas has shown that the gradual evolution of the magnetic field can lead to formation of intense, nearly force-free current layers where magnetic reconnection may be triggered (Galsgaard et al. 2003; Titov et al. 2003). In the present study, the critical parameter is the magnetization parameter defined as , which roughly corresponds to the available magnetic energy per particle. The numerical simulations presented in this paper are initialized from a force-free current layer with (Che et al. 2011; Liu et al. 2013, 2014), which corresponds to a magnetic field with magnitude B0 rotating by 180° across the central layer with a half-thickness of λ. No external guide field is included in this study but there is an intrinsic guide field By associated with the central sheet. The plasma consists of electron–positron pairs with mass ratio . The initial distributions are Maxwellian with a spatially uniform density n0 and a thermal temperature ( ). Particles in the central sheet have a net drift to represent a current density that is consistent with . Since the force-free current sheet does not require a hot plasma component to balance the Lorentz force, this initial setup is more suitable to study reconnection in low-β and/or high-σ plasmas. The full particle simulations are performed using the VPIC code (Bowers et al. 2009) and NPIC code (Daughton et al. 2006; Daughton & Karimabadi 2007), both of which solve Maxwell equations and push particles using relativistic approaches. The VPIC code directly evolves electric and magnetic fields, whereas in the NPIC code the fields are advanced using the scalar and vector potentials. Although the two codes have very different algorithms, all of the key results are in good agreement for this study, thus providing additional confidence in our conclusions. In addition, we have developed a particle-tracking module to analyze the detailed physics of the particle energization process. In the simulations, we define and adjust σ by changing the ratio of the electron gyrofrequency to the electron plasma frequency , . For 2D simulations, we have performed simulations with and box sizes , , and , where di is the inertial length . For 3D simulations, the largest case is with σ = 100. For high-σ cases ( ), we choose cell sizes and , so the particle gyromotion scale is resolved. The time step is chosen to correspond to a Courant number , where . The half-thickness of the current sheet is for , for σ = 400, and for σ = 1600 in order to satisfy the drift velocity . For both 2D and 3D simulations, we have more than 100 electron–positron pairs in each cell. The boundary conditions for 2D simulations are periodic for both fields and particles in the x direction, while in the z direction the boundaries are conducting for the field and reflecting for the particles. In the 3D simulations, the boundary conditions are periodic for both fields and particles in the y direction, while the boundary conditions in the x and z directions are the same as in the 2D cases. A weak long-wavelength perturbation (Birn et al. 2001) with is included to initiate reconnection. The parameters for different runs are summarized in Table 1, which also lists key results such as maximum energy of particles, spectral index, the fraction of kinetic energy converted from the magnetic energy, and the portion of energy gain arising from the perpendicular electric fields.

2019ApJ...877...35W__Hu_&_Yang_2014_Instance_1

Here, for simplicity, computational convenience, and continuity with past works (Kopparapu et al. 2017; Haqq-Misra et al. 2018), we have assumed a slab ocean with zero ocean heat transport. Ocean heat transport may have a significant effect on climate and also the location of clouds (Way et al. 2018). Dynamic ocean heat transport on completely ocean-covered worlds (i.e., no continents) leads to warmer global mean temperatures and a reduction in day–night temperature differences on synchronously rotating planets (Hu & Yang 2014; Del Genio et al. 2019). Yang et al. (2019) argue that ocean heat transport is critical for the treatment of cold, partially ice-covered planets around M dwarf stars; however, it does not have a meaningful effect on the climate and thermal emission phase curves for warm terrestrial planets (Ts ∼ 300 K) residing close to the inner edge of the habitable zone. The presence of continents also presents a significant uncertainty in the net effect of ocean heat transport on climate, as continents can reroute or eliminate day-to-night ocean heat transport entirely, resulting in climate states that are similar to those found in simulations with zero ocean heat transport (Del Genio et al. 2019; Yang et al. 2019). In this work, we have argued that clouds are likely more important in modulating phase curves for habitable planets, compared to surface energy transports. Still, ocean heat transport patterns, combined with variations in continental configurations and topographic prominences, could have significant complex feedback on the surface temperature, sea ice and snow cover, and cloud distributions, respectively, all of which affect thermal and reflected light phase curves. In future works, our group plans to couple our GCM with a dynamic ocean model to study the interactions between oceans, continents, topography, and observable features. However, the long equilibration times for a coupled dynamic atmosphere-ocean model still present a significant computational hinderance for conducting 3D simulations across wider parameter spaces, as we have done here.

2021AandA...656A..63M__Shakura_&_Sunyaev_1973_Instance_1

One possibility could be that the disk, since viewed at such high inclination, might create a “shadow” zone where part of the reflection incoming between Rin, 1 and Rin, 2 is obscured (see Fig. 10, panel a). As highlighted by the Figure, a jump in the scale height of the disk, located sufficiently far away from its inner edge, could in principle generate such self-shielding effect. A disk flared in the outer region was also put forward by Zdziarski et al. (2021) and Axelsson & Veledina (2021) in order to explain the presence of the outer reflection component and the variability associated to the iron line. However, it is not obvious whether a sudden puffing up of the disk could happen or not in a classical Shakura-Sunyaev disk, like the SAD. A jump could arise from a transition in the mechanism contributing mainly to the opacity in the disk. In the outer regions of a Shakura-Sunyaev disk, the opacity is expected to be mainly due to free-free absorption, while Thomson scattering dominates the opacity in the inner regions of the disk (see Eqs. (2.16) and (2.19) of Shakura & Sunyaev 1973). The geometrical thickness of the disk scales with the radius R following a (slightly) different relation; it is proportional to R−21/20 in the inner Thomson scattering-dominated, regions and to R−9/8 in the outer regions. At the boundary between these two zones (i.e., at a radius Rjump) a jump is expected. We calculated Rjump according to our results for Ṁin and assuming the same value for α in both equations. We found Rjump between 3 × 104 RG and 7 × 104 RG in all the epochs considered. In our fits the location of the outer reflector Rin, 2 was kept frozen to 300 RG. When the parameter is thawed, it is completely unconstrained, as shown in Fig. 11. Notwithstanding the uncertainty on Rin, 2, a boundary at beyond 104 RG is likely located too far away to produce an extra reflection component. However, a crucial role might be played by irradiation from the inner regions of the disk to the outer regions, an ingredient neglected in Shakura-Sunyaev disks. Beyond some boundary radius, we expect that the upper layers of the disk should be heated up by the impinging photons coming from the inner regions, leading to evaporation and to a larger effective scale height.

2022AandA...666A..60K__Gustafsson_et_al._2008_Instance_1

High-precision photometric measurements have shown that the present treatment of limb darkening is not sufficiently accurate and leads to systematic errors in the derived parameters of the exoplanets (e.g., Espinoza & Jordán 2016; Morello et al. 2017a; Maxted 2018). Typically, limb darkening is represented by rather simple laws, such as a linear law (Schwarzschild 1906), a quadratic law (Kopal 1950), a square-root law (Diaz-Cordoves & Gimenez 1992), a power-2 law (Hestroffer 1997), or a four-coefficients law (Claret 2000), so that when modeling the transit light curves, limb darkening is parameterized by some set of coefficients. Ideally, these coefficients should be constrained by the stellar modeling. Consequently, many libraries of limb-darkening coefficients covering a wide range of effective temperatures (Teff), surface gravity (log g), and metal-licities (M/H) have been produced (e.g., Claret 2000; Sing 2010; Claret & Bloemen 2011; Magic et al. 2015) using various radiative transfer codes, such as ATLAS (Kurucz 1993), NextGen (Hauschildt et al. 1999), PHOENIX (Husser et al. 2013), MARCS (Gustafsson et al. 2008), and STAGGER (Magic et al. 2013). However, the limb-darkening parameters diverge between different libraries and often lead to an inadequate quality of fits to the observed transit profiles (Csizmadia et al. 2013). First, this can be due to errors in limb-darkening coefficients introduced by interpolation from the grid of stellar parameters used in these libraries to the actual stellar fundamental parameters. Second, available stellar calculations may not treat mechanisms that affect limb darkening with sufficient accuracy, for instance, convection (Pereira et al. 2013; Chiavassa et al. 2017) or magnetic activity (Csizmadia et al. 2013). Therefore, limb-darkening coefficients are often left as free parameters in a least-squares fit to observed light curves (e.g., Southworth 2008; Claret 2009; Cabrera et al. 2010; Gillon et al. 2010; Csizmadia et al. 2013; Maxted 2018). While this method usually leads to a good quality fit to observed transit profiles, it introduces additional free parameters, resulting in possible biases and degeneracies in the returned planetary radii (Espinoza & Jordán 2015; Morello et al. 2017b). The way to reduce these biases and reliably determine planetary radii is to improve theoretical computations of stellar limb darkening.

2022MNRAS.511.2885B__Lu_et_al._2013_Instance_1

One of our key results is that accounting for a complete stellar mass function can explain the post-starburst preference of TDE hosts. The alternative, currently most widespread explanation is that post-starburst galaxies are initially born with an extremely steep density profile (Stone et al. 2018). Fig. 6 compares the initial conditions adopted in our runs with the ultrasteep initial conditions adopted by Stone et al. (2018) to explain the post-starburst preference, rescaled to our MBH mass according to their footnote 12 (see also the caption of our Fig. 6). The central density in our and their profile differs by several orders of magnitude, and in general it is not straightforward to imagine how star formation could generate such an extremely steep cusp. In fact, Sanders (1998) points out that, too close to the MBH, molecular clouds should get disrupted by its gravitational field preventing star formation; even if the Milky Way nucleus features a population of young stars very close to the MBH (Morris 1993; Ghez et al. 2003; Lu et al. 2013), supporting instead the idea of possible central star formation, it seems unlikely it can develop such steep profiles. For instance, in these systems the time-scale for stellar collisions and mergers can be shorter than the relaxation time-scale at small radii (10−2 pc, Stone et al. 2018). Note that if the density profile is initially very steep, relaxation processes render it milder in time, as the equilibrium configuration is the Bahcall & Wolf (1976) solution (ρ∝r−1.75): this means that the ultrasteep configuration should be in place since the starburst stage, i.e. stars should be basically born with ρ∝r−γ, γ = 2.5–2.75. In order to compare our results with the ones by Stone et al. (2018), we evolve their (monochromatic) profiles shown in Fig. 6 with ρ∝r−2.5, r−2.75 and we compare the obtained rates with our complete mass function models in Fig. 7. Even if their rates are overall larger, the early to late event rate is compatible with what we find for much milder profiles featuring a complete mass function; in fact, the ratio between the average TDE rate in the range 250–750 Myr (i.e. roughly the age of post-starburst systems) and 10–12 Gyr is equal to 8–14 for the Stone et al. (2018) cases, i.e. they are compatible with or smaller than the same ratios computed in our runs (see numbers in parenthesis in Table 1).

2018AandA...614A..48B__Keselman_&_Nusser_2012_Instance_1

The driving mechanisms and chronology of the buildup of bulges in late-type galaxies (LTGs) is an issue of key relevance to our understanding of galaxy evolution. According to our current knowledge on bulge demographics in the local universe, a large fraction of LTGs host pseudo-bulges (PBs; e.g., Gadotti 2009; Fisher & Drory 2011; Fernández Lorenzo et al. 2014) that substantially differ from classical bulges (CBs) in their spectrophotometric and kinematical characteristics. The latter resemble in many respects “old and dead” elliptical galaxies, lacking ongoing star-formation (SF), exhibit a spheroidal shape with inwardly steeply increasing surface brightness profiles (SBPs) being well approximated by the Sérsic (1963) fitting law with a high (≳3) exponent η, show stellar kinematics dominated by velocity dispersion (σ⋆) and obey the Kormendy (1977) scaling relations for normal elliptical galaxies (Fisher & Drory 2010). It is observationally established that CBs contain a super-massive black hole (SMBH) with a mass M∙ tightly correlating with their stellar mass ${\cal M}_{\star,\textrm{B}}, \sigma_{*}$M⋆,B,σ* and optical luminosity (Ho 2008; Kormendy & Ho 2013; see also Ferrarese & Merritt 2000). Traditionally, bulges were thought to invariably form early-on via violent quasi-monolithic gas collapse (Larson 1974) or mergers (Bender et al. 1992; Aguerri et al. 2001; Keselman & Nusser 2012) associated with vigorous nuclear starbursts (Okamoto 2012), with the disk gradually building up around them. Whereas this inside-out galaxy formation scenario appears consistent with important integral characteristics of CBs (e.g., their red colors), it does not offer a plausible explanation for the presence of PBs in present-day LTGs. These generally show ongoing SF, a significant degree of rotational support (Kormendy & Kennicutt 2004, for a review) and flatter/ellipsoidal shapes with nearly exponential SBPs (η≲2; e.g., Drory & Fisher 2007; Fisher & Drory 2010). Even though there is observational evidence that PBs also contain a SMBH (Kormendy et al. 2011; Kormendy & Ho 2013), in some cases revealing itself as an active galactic nucleus (AGN; e.g., Kotilainen et al. 2016; see Kormendy & Ho 2013 for a review), these do not follow the M∙ –σ* correlation for CBs, which appears to be consistent with a different formation route. Indeed, the prevailing concept on PB formation is that these entities emerge gradually out of galactic disks through gentle gas inflow spawning quasi-continuous SF and the emergence of a central bulge-like luminosity excess at their centers (e.g., Courteau et al. 1996; Carollo et al. 2001; Kormendy & Kennicutt 2004). Besides bar-driven gas inflow (e.g., Springel & Hernquist 2005), various other mechanisms, such as inward stellar migration, minor mergers with low-mass satellites, or a purely dynamical re-arrangement of the disk (Scannapieco et al. 2010; Guedes et al. 2013; Bird et al. 2012; Roskar et al. 2012; Grand et al. 2014; Halle et al. 2015) have been proposed as further contributors to PB growth along the Gyr-long secular evolution of LTGs.

2016AandA...588A..44Y__Jones_et_al._2014_Instance_3

The second issue concerns the fact that inside a given region, coreshine is not detected in all the dense clouds observed by Paladini (2014) and Lefèvre et al. (2014) and that the proportion of clouds exhibiting coreshine varies from one region to another. For instance, 75% of the dense clouds detected in Taurus exhibit coreshine, whereas in most other regions the proportion is closer to 50% (such as Cepheus, Chamaeleon, and Musca)5. On the contrary, there are for instance very few detections in the Orion region. In THEMIS, most of the scattering efficiency originates in the accretion of an a-C:H mantle. This leads to three possible explanations for the absence of detectable coreshine. The first explanation is related to the amount of carbon available in the gas phase. The abundance used by Köhler et al. (2015) relies on the highest C depletion measurements made by Parvathi et al. (2012) towards regions with \hbox{$N_{\rm H} \geqslant 2 \times 10^{21}$}NH⩾ 2 × 1021 H/cm2. Parvathi et al. (2012) highlighted the variability in the carbon depletion in dust depending on the line of sight. Thus, there may be clouds were the amount of carbon available for a-C:H mantle formation is smaller or even close to zero: such regions would be populated with aggregates with a thinner H-rich carbon mantle or no second mantle at all and thus exhibit very little or no coreshine emission. A second explanation is related to the stability of H-rich carbon in the ISM, which depends strongly on the radiation field intensity to local density ratio (Godard et al. 2011; Jones et al. 2014). In low-density regions (according to Jones et al. 2014, \hbox{$A_{V} \leqslant 0.7$}AV⩽ 0.7 for the standard ISRF), UV photons are responsible for causing the photo-dissociation of CH bonds, a-C:H → a-C. In transition regions from diffuse ISM to dense clouds (Jones et al. 2014, \hbox{$0.7 \leqslant A_{V} \leqslant 1.2$}0.7 ⩽ AV⩽ 1.2 for the standard ISRF), better shielded from UV photons and where the amount of hydrogen is significantly higher, H-poor carbon can be transformed into H-rich carbon through H atom incorporation, a-C → a-C:H. Similarly, carbon accreted from the gas phase in these transition regions is likely to be and stay H-rich. Then, in the dense molecular clouds, most of the hydrogen is in molecular form and thus not available to produce a-C:H mantles on the grains. However, this approximately matches the density at which ice mantles start to accrete on the grains, which would partly protect a-C:H layers that had formed earlier (Godard et al. 2011, and references therein). The stability and hydrogenation degree of a-C:H, as well as the exact values of AV thresholds, are both dependent on the timescale and UV field intensity. The resulting a-C ↔ a-C:H delicate balance could explain why in a quiet region such as Taurus most of the clouds exhibit coreshine, whereas in Orion, where on average the radiation field intensity and hardness are much higher, most clouds do not. A third explanation is related to the age and/or density of the clouds. In a young cloud, where dust growth is not advanced, or in an intermediate density cloud (ρC ~ a few 103 H/cm3), the dust population may be dominated by CMM grains instead of AMM(I) dust. Such clouds would be as bright in the IRAC 8 μm band as in the two IRAC bands at 3.6 and 4.5 μm, thus not matching the selection criteria defined by Pagani et al. (2010) and Lefèvre et al. (2014) and would be classified as “no coreshine" clouds.

2021MNRAS.503.6155C__Lovisari_et_al._2017_Instance_2

Galaxy clusters are the traces of the formation of the largest structures in the Universe and so reliable tools to investigate structures formation and evolution. In principle, this is possible only if and when we have full knowledge of the properties of these objects. The total mass (i.e. the total amount of the dark matter (DM), the intracluster medium (ICM), and the stellar components) is an invaluable quantity when exploring the abundances of clusters along the redshift: a standard way to infer cosmological parameters such as the mean matter density Ωm and the amplitude of matter perturbations σ8(Planck Collaboration XIII 2016). Furthermore, under the assumption of a simple self-similar model (Kaiser 1986; Voit 2005), we could derive the total mass of the clusters from a few observables in optical, X-ray, or millimetre band (Giodini et al. 2013). This approach results in a few scaling relations valuable when we are interested to obtain averaged results based on some statistics. However, it is prone to the assumed simplified approximations: hydrostatic equilibrium and isothermal and spherical distribution for DM and ICM (Bryan & Norman 1998). It is well known that the hydrostatic equilibrium in haloes is not always satisfied, due to non-thermal pressure contributions from internal motions and turbulence (see e.g. Fang, Humphrey & Buote 2009; Lau, Kravtsov & Nagai 2009; Laganá, de Souza & Keller 2010; Rasia et al. 2012; Nelson, Lau & Nagai 2014; Yu, Nelson & Nagai 2015; Biffi et al. 2016; Eckert et al. 2019; Angelinelli et al. 2020; Ansarifard et al. 2020; Gianfagna et al. 2020; Green et al. 2020), pointing out the impact that the dynamical state of those large gravitational bounded objects should have. Several attempts have been made to infer clusters dynamical state, using both observational data and simulations, by analysing the images of the emission in optical (see e.g. Ribeiro, Lopes & Rembold 2013; Wen & Han 2013) and in the X-ray band (see e.g. Rasia, Meneghetti & Ettori 2013; Lovisari et al. 2017; Nurgaliev et al. 2017; Bartalucci et al. 2019; Cao, Barnes & Vogelsberger 2020; Yuan & Han 2020) or of the diffusion of the cosmic microwave background (CMB) photons by thermal Sunyaev–Zel’dovich (tSZ) effect in the millimetre band (Cialone et al. 2018; De Luca et al. 2020, hereafter DL20), or a combination of some of them (see e.g. Mann & Ebeling 2012; Molnar, Ueda & Umetsu 2020; Ricci et al. 2020; The CHEX-MATE Collaboration 2020; Zenteno et al. 2020). Among the possibilities, we have to mention the studies of the clusters morphology in X-ray and tSZ maps. Several indicators are commonly used, such as asymmetry parameter (Schade et al. 1995), light concentration (Santos et al. 2008), third-order power ratio (Buote & Tsai 1995; Weißmann et al. 2013), centroid shift (Mohr, Fabricant & Geller 1993; O’Hara et al. 2006), strip parameter, Gaussian fit parameter (Cialone et al. 2018), and so on. They exploit the maps with different apertures and efficiencies and are applied individually or combined together, even with different weights (see e.g. Böhringer et al. 2010; Nurgaliev et al. 2013; Rasia et al. 2013; Weißmann et al. 2013; Mantz et al. 2015; Cui et al. 2016; Lovisari et al. 2017; Cialone et al. 2018; Cao et al. 2020; DL20; Yuan & Han 2020). A complementary approach is by applying thresholds on specific thermodynamic variables. Among the others, the central electron gas density and the core entropy are fairly reliable (Hudson et al. 2010). The azimuthal scatter in radial profiles of gas density, temperature, entropy, or surface brightness (Vazza et al. 2011) is also used as a proxy of the ICM inhomogeneities and correlated to the clusters dynamical state (see e.g. Roncarelli et al. 2013; Ansarifard et al. 2020). Alternatively, the projected sky separations between key positions in the images are resulting in reliable estimators of the dynamical state. Interestingly, the offsets between the bright central galaxy (BCG) and the peaks and/or the centroids of X-ray or tSZ maps are an indication of how much the relaxation condition is satisfied, with different efficiency (see e.g. Jones & Forman 1984; Katayama et al. 2003; Lin & Mohr 2004; Sanderson, Edge & Smith 2009; Mann & Ebeling 2012; Rossetti et al. 2016; Lopes et al. 2018; DL20; Ricci et al. 2020; Zenteno et al. 2020). To be mentioned also other approaches based on wavelets analysis (Pierre & Starck 1998), on the Minkowski functionals (Beisbart, Valdarnini & Buchert 2001), or on machine learning (see e.g. Cohn & Battaglia 2019; Green et al. 2019; Gupta & Reichardt 2020).

2019ApJ...881...42J__Jørgensen_1999_Instance_1

Stellar population evolution studies beyond z ≈ 1 have primarily focused on ages through studies of luminosity changes. Beifiori et al. (2017) used new data for 19 galaxies in z = 1.3–1.6 clusters obtained with the Very Large Telescope/KMOS to extend the redshift coverage of the results regarding the evolution of the mass-to-light (M/L) ratios of bulge-dominated passive galaxies. The authors used their new results together with the available literature results covering up to z = 1.3 (van Dokkum & Franx 1996; Jørgensen et al. 1999, 2006, 2014; Kelson et al. 2000; Wuyts et al. 2004; Holden et al. 2005, 2010; Barr et al. 2006; van Dokkum & van der Marel 2007; Saglia et al. 2010; Jørgensen & Chiboucas 2013) and low-redshift reference data for the Coma cluster (Jørgensen 1999; Jørgensen et al. 2006) to further solidify the evidence supporting passive evolution and a formation redshift zform ≈ 2. The formation redshift should be understood as the epoch of the last major star formation episode. At z ≈ 1 the massive (Mass > 1011 M) bulge-dominated galaxies in clusters appear to be in place and mostly passively evolving. Lower mass galaxies may still be added to the red sequence and from then on passively evolve (e.g., Sánchez-Blázquez et al. 2009; Choi et al. 2014), but see also Cerulo et al. (2016) for results supporting that the red sequence well below L⋆ is fully populated in rich clusters already at . Ultimately, the properties of galaxies mapped over a large fraction of the age of the universe, may constrain the models for building the galaxies. It is difficult to understand within the prevailing hierarchical model favored by the ΛCDM (cold dark matter) cosmology, the existence of such massive passive galaxies with relatively old stellar populations at z ≈ 1, while less massive galaxies appear to harbor younger stellar populations, e.g., Jørgensen et al. (2017, and references therein), see Kauffmann et al. (2003) for a discussion of this tension between the observational results and the hierarchical models of galaxy formation. However, more recent cosmological simulations like Illustris (Genel et al. 2014; Vogelsberger et al. 2014; Wellons et al. 2015) and UniverseMachine (Behroozi et al. 2019) find that massive quiescent galaxies can be in place by z ≳ 2.

2020ApJ...899L..10F__Tomida_et_al._2013_Instance_1

Understanding of the low-mass star formation process has been intensively studied from decades ago, mainly by theoretical work at the beginning (e.g., Larson 1969; Shu 1977; Shu et al. 1987; Inutsuka 2012). They explained that the fragmentation and condensation of molecular clouds result in forming dense cores, which undergo gravitational collapse to form stars. According to the theoretical studies, dense cores eventually harbor the first protostellar cores, the first quasi-hydrostatic object during the star formation process (hereafter the first core; e.g., Larson 1969; Masunaga et al. 1998; Tomida et al. 2013), which provide the initial condition of star formation. Recent magnetohydrodynamic (MHD) simulations demonstrated that the first core with a size of 1–100 au is formed when the central density exceeds ∼1010 cm−3 via the gravitational collapse. The first core is suggested to have a low-velocity (1–10 km s−1) molecular outflow with a wide opening angle (Machida et al. 2008), which is qualitatively different from the collimated jet driven by a mature protostar. However, it is difficult to identify such an object observationally because the first core has a short lifetime, 103–104 yr, depending on the physical condition of the parental core (Tomida et al. 2010), and does not show bright infrared emission. Although some candidates of the first core were already reported in the past decade (e.g., Chen et al. 2010, 2012; Enoch et al. 2010; Pineda et al. 2011; Pezzuto et al. 2012; Hirano & Liu 2014), the first-core phase is not fully explored observationally and it is supposed to still be the missing link between the isothermal and adiabatic collapse (i.e., prestellar and protostellar core). To search for candidates of the first core, it is essential to perform a survey-type observation toward a large number of starless cores. According to the early dense core survey with an average density of ≳105 cm−3 (Onishi et al. 2002), the lifetime of the starless phase is ∼4 × 105 yr (see also Ward-Thompson et al. 2007). The simple calculation tells us that only one out of a few × 10–100 cores harbors the first core(s).

2020AandA...641A.155V__Puglisi_et_al._2019_Instance_3

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.

2016ApJ...832..195N__Jin_et_al._2012_Instance_2

We ignore the density stratification effect in Case I, II, and IIa, because the width of the horizontal current sheet in our simulations is much shorter than the length. The simulation domain extends from x = 0 to x = L0 in the x-direction, and from y = − 0.5 L 0 to y = 0.5 L 0 in the y-direction, in the three cases, with L 0 = 10 6 m. Outflow boundary conditions are used in the x-direction and inflow boundary conditions in the y-direction. For the inflow boundary conditions, the fluid is allowed to flow into the domain but not to flow out; the gradient of the plasma density vanishes; the total energy is set such that the gradient in the thermal energy density vanishes; a vanishing gradient of parallel components plus divergence-free extrapolation of the magnetic field. For the outflow boundary conditions, the fluid is allowed to flow out of the domain but not to flow in, and the other variables are set by using the same method as the inflow boundary conditions. The horizontal force-free Harris current sheet is used as the initial equilibrium configuration of magnetic fields in Case I, 13 B x 0 = − b 0 tanh [ y / ( 0.05 L 0 ) ] 14 B y 0 = 0 15 B z 0 = b 0 / cosh [ y / ( 0.05 L 0 ) ] . The magnetic fields in the low solar atmosphere could be very strong (Jin et al. 2009, 2012; Khomenko et al. 2014; Peter et al. 2014; Vissers et al. 2015) and the magnetic field can exceed 0.15 T in both the intranetwork and the network quiet region (e.g., Orozco Suárez et al. 2007; Martínez González et al. 2008; Jin et al. 2009, 2012). In the work by Jin et al. 2012, the maximum of the field strength was found to be 0.15 T. The magnetic field could be even stronger in the active region near the sunspot. Therefore, we set b0 = 0.05 T in Case I and Case II, and b0 = 0.15 T in Case IIa. Due to the force-freeness and neglect of gravity, the initial equilibrium thermal pressure is uniform. The initial temperature and plasma density are set as T0 = 4200 K and ρ0 = 1.66057 × 10−6 kg m−3 in Case I, and T0 = 4800 K and ρ0 = 3.32114 × 10−5 kg m−3 in Case II and Case IIa. Therefore, the initial plasma β is calculated as β ≃ 0.0583 in Case I, β ≃ 1.332 in Case II, and β ≃ 0.148 in Case IIa. The initial ionization degree is assumed as Yi = 10−3 in Case I, and Yi = 1. 2 × 10−4 in Case II and IIa. The magnetic diffusion in this work matches the form computed from the solar atmosphere model in Khomenko & Collados (2012), and we set η = [ 5 × 10 4 ( 4200 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case I, and η = [ 5 × 10 4 ( 4800 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case II and IIa. The first part ∼ T−1.5 is contributed by collisions between ions and electrons, the second part ∼ T 0.5 Y i − 1 is contributed by collisions between electrons and neutral particles. Small perturbations for both magnetic fields and velocities at t = 0 make the current sheet to evolve and secondary instabilities start to appear later in the three cases. The forms of perturbations are listed below: 16 b x 1 = − pert · b 0 · sin 2 π y + 0.5 L 0 L 0 · cos 2 π x + 0.5 L 0 L 0 17 b y 1 = pert · b 0 · cos 2 π y + 0.5 L 0 L 0 · sin 2 π x + 0.5 L 0 L 0 18 v y 1 = − pert · v A 0 · sin π y L 0 · random n Max ( ∣ random n ∣ ) , where pert = 0.08, vA0 is the initial Alfvén velocity, randomn is the random noise function in our code, and Max ( ∣ random n ∣ ) is the maximum of the absolute value of the random noise function. This random noise function makes the initial perturbations for the velocity in the y-direction to be asymmetric, and such an asymmetry makes the current sheet gradually become more tilted, especially after secondary islands appear. The reconnection process is not really symmetrical in nature (Murphy et al. 2012), this is one of the reasons that we use such a noise function. Another reason is that the asymmetric noise function makes the secondary instabilities develop faster. Figure 1(a) shows the distributions of the current density and magnetic fields at t = 0 in case I.

2015ApJ...802....8A__Susa_2013_Instance_1

Early theoretical work on the formation of the first stars, mostly through high-resolution simulations, found that Pop III stars with mass MIII,* ≳ 100 M⊙ are born in isolation inside minihalos, and thus the “one massive Pop III star per minihalo” paradigm was established (Abel et al. 2002; Bromm et al. 2002; Yoshida et al. 2006). Later, however, several higher-resolution simulations began to observe the formation of binary protostar systems with a smaller mass range, MIII,* ≃ [10–40] M⊙ (Turk et al. 2009; Stacy et al. 2010). While the universality of the latter finding is in doubt (Greif et al. 2012; Stacy Bromm 2013; Susa 2013; Hirano et al. 2014; see also Becerra et al. 2015 for the formation of Pop III stars inside more massive halos), it certainly introduces very important subtleties to the old paradigm. One important aspect is that X-ray binary systems may remain after some of these stars die, and can emit X-rays very efficiently (we will quantify their X-ray emissivity in Section 3) through gas accretion rate comparable to the Eddington limit (Mirabel et al. 2011). This then could make the X-ray heating epoch occur earlier than previously thought, or even allow a model where the reionization is dominated by X-ray photons instead of UV photons. Reionization dominated by X-ray photons, due to their long mean free path, will occur much more smoothly in space than reionization by UV photons (e.g., Haiman 2011; Mesinger et al. 2013, and references therein). In addition, X-rays can heat the IGM (see, e.g., recent observational signature reported by Parsons et al. 2014), which impacts the dynamics of the IGM (e.g., Tanaka et al. 2012; Jeon et al. 2014) and δTb of IGM (e.g., Mesinger et al. 2013; Fialkov et al. 2014; Jeon et al. 2014). High-redshift X-ray binaries seem to dominate the X-ray background over the active galactic nuclei at the late stage of EoR (z ∼ 6–8), if their emissivity and spectral energy distribution (SED) is calibrated by the observed, low-z (0 ≤ z ≲ 4) X-ray binaries (Fragos et al. 2013).

2018AandA...610A..44M__Krüger_&_Dreizler_(1992)_Instance_1

The first investigations of the rotational spectra of ethyl isocyanide were carried out in 1966 by Bolton et al. (1966). The spectra of the first vibrational and torsional excited states were measured in the centimeter wave domain (Anderson & Gwinn 1968). In this initial study, the dipole moment was determined to be μa = 3.79 D and μb = 1.31 D; this value is usually large for a molecule that includes a CN group. This causes dense and intense rotational spectra in the millimeter wave range and also in the submillimeter wave range up to 900 GHz (bQ lines). Anderson & Gwinn (1968) also observed some A–E splittings due to the internal rotation motion of the methyl group. The most recent spectroscopic study is from Krüger & Dreizler (1992) who reinvestigated the internal rotation measurements and also determined hyperfine coupling parameters due to the nitrogen quadrupole. As in our previous studies of ethyl cyanide isotopologs, it was not possible to observe internal rotation and hyperfine splittings due to our Doppler limited resolution. Our analysis was rather easy, starting from a prediction based on Krüger & Dreizler (1992) parameters. First, we analyzed and fit the most intense transitions, the aRh transitions, up to 330 GHz. These transitions were shifted only a few MHz from the initial predictions. Then bR and bQ lines were searched and included in the fit up to 330 GHz. Next, all the spectra were analyzed up to 990 GHz without difficulty. For the fitting, we employed ASFIT (Kisiel 2001) and predictions were made with SPCAT (Pickett 1991). The global fits included 6 transitions from Anderson & Gwinn (1968), 29 lines from Krüger & Dreizler (1992), and 2906 from this work. The maximal quantum numbers are J = 103 and Ka = 30. Both reductions A and S were tested. A reduction permits us to check theagreement of our new parameters set with those from Krüger & Dreizler (1992) (Table 1). Using S reduction slightly decreases root mean square from 30.3 to28.7 kHz. The condition numbers are nearly the same: 295 and 310 for the A and S reductions, respectively.The A reduction requires 23 parameters, but 5 additional parameters are required for the S reduction (Table 2). For this reason we used the A reduction even if this molecule is close to the prolate limit with kappa = −0.9521. Part of the new measurements are in Table 3. Owing to its large size, the complete version of the global fit Table S1 is supplied at the CDS. The fitting files .lin (S2), .par (S3), and the prediction .cat (S4) are also available at CDS.

2021ApJ...908...15S__Scoville_et_al._2014_Instance_1

Several methods are commonly used to estimate gas masses. The first method is using CO emission line fluxes (e.g., Daddi et al. 2010; Genzel et al. 2010; Tacconi et al. 2010, 2013). This method has uncertainties on the CO-to-H2 conversion factor, which changes depending on metallicity (Genzel et al. 2012), and on the CO excitation states when higher-J CO lines are used (e.g., Daddi et al. 2015; Riechers et al. 2020). Furthermore, observations of CO lines for galaxies at high redshifts are observationally expensive. The second method is converting a dust mass into a gas mass with an assumed gas-to-dust mass ratio (e.g., Santini et al. 2014; Béthermin et al. 2015). Because the gas-to-dust mass ratio depends on the metallicity (Leroy et al. 2011; Rémy-Ruyer et al. 2014), metallicity measurements are crucial for estimating the gas mass accurately. Gas masses can also be estimated with an empirically calibrated relation between single-band dust continuum flux at the Rayleigh–Jeans (R-J) tail and gas mass (e.g., Scoville et al. 2014, 2016; Groves et al. 2015). These scaling relations are calibrated with local galaxies or with local galaxies and SMGs up to z ∼ 2. In this method, the gas-to-dust mass ratio is included in the conversion factor, and therefore does not need to be considered. It remains unclear whether the empirical calibration methods are applicable to galaxies at z > 3 or how much scatter there is in the relationships. Given that dust continuum observations take much less time compared to the CO observations, using dust continuum as a tracer of gas has the advantage of increasing the number of galaxies at higher redshifts with individual gas estimates, but these will only be reliable when precise metallicities are available as well. Metallicity measurements based on rest-frame optical emission lines for dustier star-forming galaxies are thought to have larger uncertainties due to strong dust obscuration (e.g., Santini et al. 2010). Herrera-Camus et al. (2018) reported a discrepancy between metallicities derived with rest-frame optical emission lines and far-infrared (FIR) fine-structure lines for local (ultra) luminous infrared galaxies ((U)LIRGs).

2017AandA...597A.114B__Bergin_&_Tafalla_(2007)_Instance_1

The band-merged Hi-GAL product catalogue (Elia et al. 2016) is built as in Elia et al. (2013) and provides spectral energy distribution (SED) fit parameters to the individual clumps. The average angular size of the clumps is 25′′ at 250 μm. Using the heliocentric distances provided in the Hi-GAL product catalogue (described in Sect. 3.2.) and the SED fit parameters, the authors of the catalogue are able to provide linear diameters and masses of the clumps. The catalogue explores a wide range of linear diameters and masses, from sub-parsec (≤ 0.1 pc) to parsec scale (1−5 pc) with masses from 1 M⊙ to 105M⊙. These wide ranges mean that we probably mix several types of objects, from single star-forming cores to clumps containing multiple cores, even to entire clouds, depending on the distance of object. Most of these sources, however, fulfil the definition of clump, according to the definition of Bergin & Tafalla (2007). Dust temperatures for these clumps have been estimated through a grey-body fit, and searched in the range T = 5−40 K. Most of them are found between 10 and 20 K. The appearance of the SED and the parameters obtained through the grey-body fit allow for classifying the evolutionary stage of these objects. Three stages are identified: starless unbound and bound (pre-stellar) objects, and proto-stellar objects. The pre- and proto-stellar stages are distinguished from each other by the presence of a 70 μm source in a proto-stellar clump (e.g. Dunham et al. 2008; Ragan et al. 2012; Veneziani et al. 2013). The bound versus unbound identification is obtained by using the mass-radius relation, well known as Larson’s third law, originally formulated as M(r) > 460 M⊙(r/ pc)1.9, with r the radius of the source (Larson 1981). Beyond 4−5 kpc two effects could lead to misclassifying the pre- and proto-stellar stages. First, different sensitivities of PACS and SPIRE could lead to missing a possible 70 μm counterpart of a source detected with SPIRE. Second, at large heliocentric distances, two or more pre- and proto-stellar sources could be detected as a single object as a result of lacking resolution, globally and simply labelled as proto-stellar. The first effect was partially mitigated by searching for a possible 70 μm counterpart that was not originally listed in the single-band catalogue through performing additional source detection at this band using a threshold less demanding than the initial one. Elia et al. (2016) provide statistics and a discussion about the ratio between pre- and proto-stellar clumps. The distribution of the three evolutionary stages is shown in a portion of the Galactic Plane in Fig. 1. Each panel represents an evolutionary stage in the longitude range 26 ≤l≤ 31 deg. The pre-stellar clumps are more extended in Galactic latitude than the proto-stellar clumps. The unbound clump distribution is hard to characterise because it is obscured by the proto-stellar clumps in the mid-plane, therefore we only consider clustered over-densities composed of pre- and proto-stellar clumps. These distributions are observed across the entire longitude range of this study.

2022MNRAS.516.2641V__Soleri_&_Fender_2011_Instance_1

The connection between the accretion flow and compact jets is routinely studied in the X-ray–radio luminosity (LX–LR) diagram. Here, the former traces the accretion luminosity and is a proxy for accretion rate, while the latter traces the jet and its brightness. Compact BH jets show an LX–LR correlation across ∼eight orders of magnitude in X-ray luminosity (Hannikainen et al. 1998; Corbel et al. 2000, 2003; Gallo et al. 2006); a subset of sources follows a radio-bright correlation with a power-law slope of β ≈ 0.6 (where $L_R \propto L_X^{\beta }$, while others follow a steeper correlation with β ≳ 1 at high X-ray luminosities, before re-joining the other track around LX ≈ 1035 erg s−1 (e.g. Coriat et al. 2011; Soleri & Fender 2011; Carotenuto et al. 2021, although discussion exists regarding the statistical robustness of this separation; e.g. Gallo, Degenaar & van den Eijnden 2018). The situation for NS LMXBs is even more complex. While, as a sample, NS LMXBs are ∼22 times radio-fainter than BH systems, the individual NS systems do not appear to follow a single correlation; significant scatter exists both between sources and between outbursts of the same source. Furthermore, due to their radio faintness, few sources have been monitored over a large range of X-ray luminosity – particularly below LX ≈ 1035 erg s−1, few NS LMXBs have been detected in radio (e.g. Tudor et al. 2017; Gusinskaia et al. 2020). For the sample of NS LMXBs, however, a power-law slope of β ≈ 0.4– −0.5 has been measured (Gallo et al. 2018), which is similar to BHs. Different radiative efficiencies may be expected for the two types of LMXBs–BHs can advect a fraction of the liberated gravitational energy across the event horizon, while the presence of an NS surface implies that all this liberated energy should, eventually, be radiated. Therefore, a similarity in the LX–LR coupling of the two source classes, which depends on this radiative efficiency, is surprising.

2016MNRAS.462.3441D__Namouni_1999_Instance_5

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

2020AandA...643A..92S__Huang_et_al._2018a_Instance_1

Our models show that high signal to noise ratio (S/N) spiral structures in the gas dynamics, together with a spiral seen in the dust continuum surface density, can put constraints on grain size comparing the observed amplitude of the spiral in the dust with that in the gas. There are multiple candidate sources that are known to have spirals in the continuum or the gas. However, to give an accurate estimate of the surface density and to derive a velocity residual map, we need data with high spatial and velocity resolution. Out of the recent high-resolution Disk Substructures at High Angular Resolution Project (DSHARP) survey of 20 disks, 3 were found to harbor spirals: Elias 27, WaOph 6, and IM Lup (Huang et al. 2018a). Elias 27 harbors the highest contrast spiral arms of the survey, but gravitational instability is a convincing explanation for this source (Pohl et al. 2015; Meru et al. 2017; Forgan et al. 2018, although see also Hall et al. 2018), which makes it unusable for this purpose, as further discussed in Sect. 5.3. Furthermore, Elias 27 and WaOph 6 suffer from cloud and outflow contamination in the CO gas (Andrews et al. 2018), which makes it impossible to use the kinematic data published so far. The IM Lup disk has two high contrast spiral arms in the millimeter continuum, and no cloud contamination (Andrews et al. 2018), so is an ideal candidate to analyze. Unfortunately, no spiral signal is detected in the gas for the IM Lup disk, but in Sect. 4.3 we will show that we can make a prediction on what the spiral looks like in the two velocity components. Other sources are considered by surveying the literature. MWC 758 is a promising source (Dong et al. 2018; Boehler et al. 2018), but the observations are currently too limited to be used. HD 100453 has a spiral detected in scattered light, continuum, and CO gas (Benisty et al. 2017; Rosotti et al. 2020), but the spiral seen in the gas is beyond the size of the continuum disk and the inner region of the disk has a noisy velocity map, potentially due to a warp in the inner disk, which makes the source not useful in our analysis. TW Hya harbors the only spirals in gas dynamics, potentially in the azimuthal velocity (Teague et al. 2019). The spirals are detected in the CO gas emission as well, but no spiral signal is present inthe dust continuum images. In the next subsection, we will analyze the TW Hya disk further and show that the observed spiral signal in the dynamics is interesting for further study, but not useful to test our model.

2016MNRAS.462.1415C__Bolzonella_et_al._2010_Instance_1

Two major limitations, often neglected, affect this type of analysis: the adoption of oversimplified models to describe the wide variety of observed galaxy SEDs and the presence of ‘systematic’ model uncertainties. This second limitation has been addressed in several studies already (e.g. Charlot, Worthey & Bressan 1996; Cerviño, Luridiana & Castander 2000; Conroy, Gunn & White 2009; Percival & Salaris 2009; Conroy & Gunn 2010; Conroy, White & Gunn 2010a). The difficulty of precisely quantifying systematic model uncertainties has led to mainly qualitative conclusions, leaving the problem unsolved. The first limitation is easier to tackle, for example, by using more physically realistic models of galaxy SEDs and combining these with advanced statistical techniques to extract physical constraints from data. This appears as the most promising route to fully exploit the information gathered by modern photometric and spectroscopic galaxy surveys. Yet, the several tools proposed so far to interpret galaxy SEDs in terms of physical parameters do not allow one to fully exploit the high quality of modern data. For example, most existing approaches rely on the adoption of a rigid physical model (e.g. analytic, two-parameter star formation histories combined with a standard dust attenuation curve and the assumption that all stars in a galaxy have the same metallicity) to describe galaxy SEDs (e.g. Bolzonella et al. 2010; Wuyts et al. 2011; Hernán-Caballero et al. 2013; Ilbert et al. 2013; Bauer et al. 2013; Muzzin et al. 2013; Lundgren et al. 2014; Kochiashvili et al. 2015; Mortlock et al. 2015; Kawinwanichakij et al. 2016). Even with the inclusion of superimposed bursts of star formation (e.g. Kauffmann et al. 2003; Gallazzi et al. 2005; Pozzetti et al. 2007; Gallazzi & Bell 2009; da Cunha et al. 2010), this does not allow a physically consistent description of the contributions by stars, gas and dust to the integrated emission from a galaxy, nor the inclusion of a potential AGN component (a notable exception is the approach of Pacifici et al. 2012, who incorporate star formation and chemical enrichment histories from numerical simulations of galaxy formation and emission from photoionized gas). Also, current spectral analysis tools are generally optimized to interpret either photometric or spectroscopic observations of galaxies, but not arbitrary combinations thereof. Finally, most existing tools suffer from additional limitations: many focus on the selection of ‘best-fitting’ parameters rather than on the uncertainties associated with these parameters (e.g. chi-square minimization techniques; Arnouts et al. 1999; Bolzonella, Miralles & Pelló 2000; Kriek et al. 2009); when this is not the case, the number of free parameters that can be explored is generally limited (e.g. with grid-based Bayesian techniques; da Cunha, Charlot & Elbaz 2008; Noll et al. 2009; Pacifici et al. 2012); and when more sophisticated (e.g. Markov Chain Monte Carlo, hereafter MCMC) techniques allow the exploration of more parameters, instrumental effects are generally not incorporated in the analysis (e.g. Acquaviva, Gawiser & Guaita 2011; Serra et al. 2011; Han & Han 2014).

2018MNRAS.477.3520L__Abolfathi_et_al._2018_Instance_2

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

2018ApJ...854...73I__Schenker_et_al._2013_Instance_1

The best-fit parameters are shown in Table 3. We find that the uncertainties in M* and are considerably large due to a degeneracy between the two parameters when all parameters are variable. Plotted in Figure 1 is the faint-end slope α as a function of redshift. Our results indicate that the best-fit values of α are about −2 at to 10, which are steeper than those at lower redshift (e.g., at in Bouwens et al. 2015). We show the fitting results at , 8, 9, and 10 in Figures 2, 3, 4, and 5, respectively. The top and bottom panels present the observed number densities and the best-fit luminosity functions in the image plane and the source plane, respectively. We also plot the results of previous blank-field surveys (Ouchi et al. 2009; Bradley et al. 2012; McLure et al. 2013; Oesch et al. 2013; Schenker et al. 2013; Bowler et al. 2014; Bouwens et al. 2015; Finkelstein et al. 2015; Calvi et al. 2016) and recent HFF results in other studies (Atek et al. 2015a; Laporte et al. 2016; McLeod et al. 2016). The best-fit parameters are consistent with those in previous studies. In the top panel of Figure 2, there may be an excess in the observed surface number density at . The reason for this excess is not clear, although using a size–luminosity relation that gives smaller sizes at faint magnitudes may reduce this excess. At , the observed number densities at the bright end are slightly larger than the number densities in the simulation. This is probably due to the existence of an overdense region of dropouts in the Abell 2744 cluster field. We discussed the properties of the overdensity in Ishigaki et al. (2016; see also Zheng et al. 2014 and Atek et al. 2015b). At , although we detect no galaxies, we can place a constraint on the luminosity function from the non-detection. Based on the best-fit parameters where only is variable, ∼1.4 galaxies are expected to be detected in the HFF fields. The middle panels of Figures 2–5 show histograms of the number of the dropouts. It is seen that our samples push the magnitude limits of the luminosity functions significantly by up to ∼3 magnitude. The correlations between M* and α at z ∼6–7 and 8 are presented in Figure 6.

2020AandA...642A..19M__Tempel_et_al._(2014a)_Instance_1

While this large-scale structure of the Universe (LSS) is also composed of dark matter and gas, it was through the galaxy distribution that it has started to be detected. Galaxy clusters were the first cosmic web features to be identified and studied because they are easily detectable through various techniques. Only with the advent of wide-area spectroscopic redshift surveys have other structures such as filaments begun to be systematically identified. Surveys such as the Two-Degree Field Galaxy Redshift Survey (2dFGRS, Colless et al. 2001), the Sloan Digital Sky Survey (SDSS, York et al. 2000), the Galaxy And Mass Assembly survey (GAMA, Driver et al. 2009), the Vimos Public Extragalactic Redshift Survey (VIPERS, Scodeggio et al. 2018), or the COSMOS survey (Scoville et al. 2007) have allowed us to obtain statistical samples of filaments and other LSS features. For example, Chen et al. (2016) and Tempel et al. (2014a) have produced filament catalogues in the SDSS (but see also the works by Aragón Calvo 2007; Sousbie et al. 2011; Rost et al. 2020; Shuntov et al. 2020; Kraljic et al. 2020, some of which also used the same algorithm as we used here). Other works such as Kraljic et al. (2018) and Alpaslan et al. (2014) detected filaments in GAMA, while Malavasi et al. (2017) detected filaments in VIPERS. Additionally, Gott et al. (2005), Iovino et al. (2016), and Kraljic et al. (2018) also identified walls in the SDSS, COSMOS, and GAMA surveys, respectively, while several projects are devoted to the analysis of voids (see e.g. Colberg et al. 2008, for a summary). Recently, not only spectroscopic surveys, but also the increased precision of photometric redshifts (e.g. in COSMOS and in the Canada-France-Hawaii Telescope Legacy Survey, CFHTLS, Laigle et al. 2016; Coupon et al. 2009) allowed for the detection of filaments in volumes of the Universe up to z ∼ 1 (Laigle et al. 2018; Sarron et al. 2019, but also Darvish et al. 2014).

2020MNRAS.492..444Y__Proga_&_Begelman_2003_Instance_1

In spite of these similarities, the angular profiles of these properties in two-dimensional simulations reveal a totally different accretion pattern. Within the stagnation radius, winds are mainly accreted in the polar region as a result of the low angular momentum there, whereas material with high angular momentum in the mid-plane outflow is subject to outflow, because the angular momentum of the stellar winds at a given radius decreases towards the pole. This ‘funnel’ accretion scenario is also found in other simulations with a spherical-like distribution of rotationally accreting material (Proga & Begelman 2003; Ressler et al. 2018). The density and temperature at the equatorial plane are higher than those in the polar region, and thus the highest mass accretion rate is found at the boundary of the polar and disc regions. Therefore the accretion pattern of the two-dimensional simulations is totally different from the inflow–outflow structure in the one-dimensional calculations. Another difference between our results is that the parameters of our best-fitting model, fq and vw, sn, are generally smaller than those in one-dimensional calculations. The smaller fq can be explained in three ways. First, our BH mass is double that in the one-dimensional case, which results in a deeper gravitational potential that allows for a more massive gas reservoir. Thus fq should be smaller to achieve the observed gas density. Second, our outer boundary is set at 17 arcsec, which is larger than the radius of the one-dimensional outer boundary of 12 arcsec. The larger outer boundary causes the mass injection rate by stellar winds to increase by 50 per cent. Because stellar winds are the only mass source in our models, a smaller fq is required to cancel out this extra mass injection. Third, a smaller vw, sn could also lead to a smaller fq owing to less efficient heating by supernova explosions. The smaller vw, sn is constrained by the temperature at large radii, especially the temperature between 10 and 20 arcsec, which is not fitted in the one-dimensional calculations.

2017ApJ...850...18H__Murase_et_al._2015_Instance_1

The heating due to the reprocessing of non-thermal photons produced in the nebula can be efficient even at late times. Here, we treat these processes in a simple way as follows. At early times, electromagnetic cascades proceed in the saturation regime, leading to a flat energy spectrum up to ∼1 MeV (Metzger et al. 2014). At later times, the spectrum depends on the seed photon spectra, but it can roughly be estimated to be a flat spectrum from ∼1 eV to ∼0.1 TeV, while the supernova emission continues, which is expected based on more detailed calculations (e.g., Murase et al. 2015; K. Murase et al. 2017, in preparation). High-energy γ-rays (≳1 MeV) heat up the ejecta through the Compton scattering and the pair production process. X-ray and UV photons are absorbed and heat up the ejecta through the photoelectric (bound-free) absorption unless the ejecta are fully ionized. Here, we describe the heating rate as 12 Q ˙ rad ( t ) ≈ ( f γ + f X − UV , bf ) L sd , where fγ and f X − UV , bf are the heating efficiencies of γ-rays and X-ray and UV photons to the spin-down luminosity, respectively. We calculate the frequency dependent heating efficiency of γ-rays at each time: 13 f γ ( t ) = ∫ ν min ν max d ν ν min ( K γ , ν τ γ , ν , 1 ) ∫ 1 eV 1 TeV d ν ν , where the frequency range of γ-rays is ( h ν min , h ν max ) = ( 10 keV , 1 TeV ) , and h is the Planck constant. Here, τγ, ν is the optical depth of the ejecta to γ-rays and Kγ, ν is the photon inelasticity at a given frequency, where the Klein–Nishina cross section and the cross section for the Bethe–Heitler pair production in the field of a carbon nucleus are taken into account (Chodorowski et al. 1992; Murase et al. 2015). Note that the coefficient of the γ-ray optical depth depends on the density profile of the ejecta. Here, we simply assume a density profile to be constant with the radius. Adopting a realistic density profile may result in different ejecta mass and velocity estimates by a factor of a few.

2020ApJ...901...41S__Harrington_1973_Instance_1

Observations have shown that the shape of the Lyα line is diverse. It includes broad damped absorption profiles, P-Cygni profiles, double-peak profiles, pure symmetric emission profiles, and combinations thereof (Kunth et al. 1998; Mas-Hesse et al. 2003; Shapley et al. 2003; Møller et al. 2004; Noll et al. 2004; Tapken et al. 2004; Venemans et al. 2005; Wilman et al. 2005). This variety can be understood through a detailed radiative transfer calculation, which is analytically solvable only for simple cases (e.g., a static, plane-parallel slab by Harrington 1973 and Neufeld 1990, and a static uniform sphere by Dijkstra et al. 2006). Later, numerical algorithms based on Monte Carlo techniques were developed to solve radiative transfer for more general cases. Now theoretical studies mostly rely on them (e.g., Spaans 1996; Loeb & Rybicki 1999; Ahn et al. 2000, 2002; Zheng & Miralda-Escudé 2002; Richling 2003; Cantalupo et al. 2005; Dijkstra et al. 2006; Hansen & Oh 2006; Tasitsiomi 2006; Verhamme et al. 2006, 2015; Laursen et al. 2013; Behrens et al. 2014; Duval et al. 2014; Gronke et al. 2015; Smith et al. 2019; Lao & Smith 2020; Michel-Dansac et al. 2020). Meanwhile, a galaxy model needs to be constructed to perform such a radiative transfer calculation. One can adopt a realistic galaxy model from hydrodynamical simulations. Galaxies from such simulations can be useful for performing a statistical study of Lyα properties, but they cannot be directly used to model individual galaxies in observations. Therefore it would be better to adopt a simple but manageable toy model for the purpose of reproducing observations. One example for such models is a shell model, in which a central Lyα source is surrounded by a constantly expanding, homogeneous, spherical shell of H i medium with dust. Although this shell model has surprisingly well reproduced many observed Lyα line profiles (e.g., Ahn 2004; Schaerer & Verhamme 2008; Verhamme et al. 2008; Schaerer et al. 2011; Gronke et al. 2015; Yang et al. 2016; Gronke 2017; Karman et al. 2017), because of its extreme simplicity and contrivance, there is still room for improvement (e.g., see Section 7.2 in Yang et al. 2016; Orlitová et al. 2018).

2020MNRAS.496.1051A__Rudolph_et_al._2006_Instance_2

The radial distribution of S/H ratios and the corresponding gradient are shown in panel (b) of Fig. 12. We obtain a slope of −0.035 ± 0.006 dex kpc−1 (very similar to the one we obtain with the ICF of ADIS20, as can be seen in Table 8), which is consistent with the one of −0.041 ± 0.014 dex kpc−1 estimated by Rudolph et al. (2006) using FIR lines and also very similar to the slope of our O/H gradient. We report a dispersion around the S/H gradient of 0.10 dex, somewhat larger than the individual observational uncertainties. Recently, Fernández-Martín et al. (2017) reported a slope of −0.108 ± 0.006 dex kpc−1 using optical spectra for H  ii regions located at RG between 5 and 17 kpc. That value of the slope is considerably much steeper than our determination and other previous estimates from the literature (e.g. Shaver et al. 1983; Simpson et al. 1995; Afflerbach, Churchwell & Werner 1997; Rudolph et al. 2006). Esteban & García-Rojas (2018) observed some H ii regions with a very low ionization degree – O2 +/O 0.03 – but measurable [S iii] 6312 or 9069 Å lines. For such nebulae is possible to assume that S/H ≈ S+/H+ + S2 +/H+, since the contribution of S3 +/H+ in those objects is expected to be negligible. The objects with such properties are IC 5146, Sh 2-235, Sh 2-257, and Sh 2-271, whose total abundances of S/H range from 6.70 to 7.01 but, unfortunately, cover a rather narrow range of RG – between 9.3 and 11.7 kpc – and no confident gradient can be estimated with so small baseline. Inspecting Tables 3 and 4 we can see that there is a sizable group of low-ionization degree H ii regions that lack of determination of their S2 +/H+ ratios. This is because the rather faint auroral [S iii] 6312 Å line could not be detected in those objects. We plan to obtain additional optical spectra covering the bright nebular [S iii] 9069, 9532 Å lines of that group of nebulae for trying to increase the number of objects with S/H ratios determined without ICF and estimate a more precise gradient for this element.

2021ApJ...917...24Z__Coughlin_et_al._2020b_Instance_2

Our simulation results show that the median detectable distances of targeted GW events from BH–NS mergers for a single 2nd generation GW detector and a network of such detectors are ∼300 Mpc and ∼700 Mpc, respectively (see Table 4). For comparison, Figure 12 shows that the detection rate and detectable distance for HLV (O3) are approximately the same as those for the case when only bKAGRA is running. This is basically consistent with the detection rate and the distance distribution of BH–NS merger candidates detected during LVC O3 (e.g., Anand et al. 2020; Antier et al. 2020b; Coughlin et al. 2020b; Gompertz et al. 2020; Kasliwal et al. 2020). In Section 3, we have shown that the kilonova absolute magnitude at 0.5 days after a BH–NS merger is mainly distributed in the range ∼–10 to ∼–15.5. In view of the fact that the limiting magnitude of the follow-up wide-field survey projects is almost ≲21 mag (e.g., Antier et al. 2020b; Gompertz et al. 2020; Coughlin et al. 2020b; Kasliwal et al. 2020; Wyatt et al. 2020), the maximum detectable distance for BH–NS kilonovae would be ≲200 Mpc, which can hardly cover the horizon of GW-triggered BH–NS merger events that O3 found (as shown in Figure 10). However, although BH–NS merger kilonovae can hardly be detected for the present search depths, Figures 9 and 11 reveal that there are great opportunities to discover on-axis afterglows associated with sGRBs or orphan afterglows if the BH components have a high-spin distribution. In order to cover the distance range for searching for BH–NS kilonovae for the network of 2nd generation GW detectors as completely as possible, a search limiting magnitude mlimit ≳ 23–24 is required as shown in Figure 10. Present survey projects could reach this search limiting magnitude by increasing exposure times and the number of simultaneous exposures. However, the GW candidates during O3 had very large localization areas with an average of thousands of square degrees (Antier et al. 2020b). Increasing exposure times makes it hard for the present survey projects to cover such large localization areas. Therefore, during the HLVK era, we recommend that survey projects may search for jet afterglows after GW triggers with a relatively shallow search limiting magnitude. If BH–NS mergers have a high location precision, a limiting magnitude of mlimit ≳ 23–24 can be reached, which gives a higher probability of discovering associated kilonovae.

2022MNRAS.515.2188H__Rorai_et_al._2018_Instance_2

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

2018ApJ...867..101B___2018b_Instance_1

To fully describe a population of streams, we need a realistic model of the Galaxy. The latest generation of hydrodynamical simulations have produced models that well reproduce a multitude of features observed in galaxies, both as individual objects (e.g., Wetzel et al. 2016) and as a population (e.g., Nelson et al. 2018; Pillepich et al. 2018). Part of their success is in the achieved high resolution, so at the present a Milky-Way-like galaxy is modeled by up to 140 million particles (Wetzel et al. 2016). Ultimately, we would like to have a description of a galaxy that is representative of these models, but at a fraction of the numerical cost. In the classic study, Hernquist & Ostriker (1992) developed a set of basis functions for density and gravitational potential that can reproduce complex morphology of galaxies with a small number of terms (e.g., Lowing et al. 2011; Lilley et al. 2018a, 2018b). These expansions reproduce the force field of an N-body simulation with a precision of a few percent, so representing the gravitational potential in our model with an expansion of basis functions is tempting. However, a truly realistic solution needs to accurately capture not only the current structure of the Galaxy but also its evolution in time. Even though the Milky Way has had a relatively quiet recent merger history, it is currently undergoing a major merger with the Large Magellanic Cloud (e.g., Besla et al. 2007; Peñarrubia et al. 2016), which has been a source of gravitational perturbation for at least a billion years—sufficiently long to affect stellar streams. Specifically, Law & Majewski (2010) showed that the only static, ellipsoidal halo that reproduces both the positions and radial velocities along the Sagittarius stream is triaxial. This model correctly predicted proper motions along the stream (Sohn et al. 2015), so it appears to be describing the effective potential well, even though it is cosmologically improbable (Debattista et al. 2013). On the other hand, modeling Sagittarius in a combined system of the Milky Way and the LMC relaxes the requirement for the dark matter halo to be triaxial (Vera-Ciro & Helmi 2013), signaling that having a model of the potential that is correct on average is no guarantee of recovering the true halo shape. To ensure that the complexities added to the model are realistic, the basis function expansion should thus be time dependent, simultaneously describing the interaction between the Milky Way and the LMC, while maintaining a thin and old disk. We relegate the development of such a model and its implementation for mapping the dark matter in the Galaxy to a future study.

2021AandA...655A..99D__Carigi_et_al._2005_Instance_2

Another way of obtaining information about the nucleosynthesis processes involved in producing carbon is to compare it with other elements that are characterised by a well-known source of production, as in the case of oxygen. In Fig. 5, we show the variation of [C/O] as a function of [Fe/H], which serves as a first-order approximation to the evolution with time. To calculate the [C/O] ratios, two oxygen abundance indicators are used independently. At subsolar metallicities, the abundance ratios with both oxygen indicators are mostly negative and show an increasing trend towards higher metallicity. This is explained by the fact that oxygen is entirely produced by SNe Type II from massive progenitors, which started to release theiryields at earlier ages in the Galaxy and, hence, at lower metallicities (e.g. Woosley & Weaver 1995). The massive stars producing carbon at low metallicities might be less massive than those producing oxygen (i.e. having a longer life), explaining a delayed contribution of carbon, hence, the negative [C/O] ratios. Alternatively, this could be explained by increasing O/C yields for more massive progenitors of SNeII. Once metallicity starts to increase, low- and intermediate-mass stars release carbon and massive stars start to eject more carbon than oxygen (Carigi et al. 2005). The [C/O] ratio seems to have a constant rise towards higher metallicities when using the forbidden oxygen line. However, in the case when the O I 6158 Å line is employed, we do observe that the maximum in [C/O] takes places close to solar metallicity to then become flat or decrease. This suggests that low-mass stars mostly contribute to carbon around solar metallicity, whereas at super-solar metallicities, massive stars produce carbon together with oxygen, thereby flattening or even decreasing the [C/O] ratio. This trend is in agreement with the metallicity dependent yields from Carigi et al. (2005), which provide higher carbon as [Fe/H] increases from massive stars (i.e. also increasing the O production) but lower carbon from low and intermediate mass stars as [Fe/H] increases (i.e. less production of C). The turning point of increased relative production of carbon from massive stars takes place at A(O) ~ 8.7 dex (see Fig. 2 of Carigi et al. 2005) which equals to [O/H] ~ 0.0 dex. This observed behaviour of [C/O] is in contrast to the steady increase of [C/O] up to [Fe/H] ~ 0.3 dex found, for example, by Franchini et al. (2021). Nevertheless, the general trend we find when using the [O I ] 6300 Å line is similar to the reported by Franchini et al. (2021), who use also that oxygen indicator. All thick-disk stars present negative [C/O] ratios and when using the oxygen line at 6158 Å thin-disk stars with [Fe/H] ≲ –0.2 have [C/O] 0 as well. Thick-disk stars and low-metallicity thin-disk stars at the same metallicity have similar [C/O] ratios, meaning that the balance between different production sites for oxygen and carbon is the same among both populations, despite [C/Fe] and [O/Fe] being systematically higher for thick-disk stars at a given metallicity.