adsabs/FOCAL huggingface dataset:
Throughout their short lifetimes, high-mass stars (>8 M⊙) inject large amounts of energy and momentum into their host environments through a variety of feedback processes (e.g. Krumholz et al. 2014). The most potentially disruptive of these feedback mechanisms occurs when the stars eventually die, exploding as supernovae (SNe). Indeed, SNe are thought to play a major role in the self-regulation of star formation in galaxies through their contribution to the total energy and momentum budget of the interstellar medium (ISM; McKee & Ostriker 1977; Mac Low & Klessen 2004; Klessen & Glover 2016). As the rate of cooling in the ISM is proportional to the gas density squared, the efficiency with which SNe inject energy and momentum into the local galactic environment strongly depends on the density distribution of the gas into which they explode (see Girichidis et al. 2016 and references therein). For example, SNe that explode within dense molecular clouds may be limited to disrupting their natal gas clouds, whilst SNe that explode into lower density environments can drive hot expanding bubbles to much larger distances (tens to hundreds of pc) and influence galactic scale processes (e.g. kpc-scale galactic outflows; Veilleux, Cecil & Bland-Hawthorn 2005; Agertz et al. 2013; Stinson et al. 2013; Keller, Kruijssen & Wadsley 2020; Veilleux et al. 2020). Feedback from the pre-SNe stages of high-mass stars plays a significant role in determining the environment into which SNe subsequently explode. Simulations have long predicted that this ‘pre-processing’ can potentially even destroy the host molecular cloud before the first SN explosion (e.g. Dale, Ercolano & Bonnell 2012, 2013), and observations of molecular clouds and H ii regions in nearby galaxies now show that pre-SN feedback is primarily responsible for the destruction of molecular clouds across the local galaxy population (Kruijssen et al. 2019b; Chevance et al. 2020b,c). Studying the effects of these earliest stages of stellar feedback on their environment is then crucial to quantifying the contribution of SNe in driving the galaxy-scale energy and momentum cycle of the ISM in galaxies. In light of this, significant observational effort has been invested to better disentangle and quantify the effect of various feedback mechanisms within young stellar systems (e.g. Oey 1996a,b; Pellegrini, Baldwin & Ferland 2010, 2011). More recent efforts have focused on measuring and comparing the internal pressure components from different feedback mechanisms in H ii regions located within the Small and Large Magellanic Clouds (SMC and LMC, respectively), such as the well-known 30 Doradus complex (e.g. Lopez et al. 2011, 2014; Chevance et al. 2016; McLeod et al. 2019), as well as other nearby galaxies (e.g. McLeod et al. 2020). These studies have provided important insights into early-stage feedback, but further work is needed to understand how pre-processing varies with environment, particularly to higher density, pressure, and metallicity regimes such as those in galactic nuclei and high-redshift galaxies.
Citation: Stinson et al. 2013
Citation function: Background
We observed our full sample of all 24 candidate highly variable AGN with Swift (Gehrels et al. 2004) for ~2 ks each, between 2010 and 2014 as part of a fill-in programme. All XRT (Burrows et al. 2005) observations were made in photon counting mode with exposure times ranging from 1.6–3.7 ks. The Swift-XRT data were obtained from the UK Swift Science Data Centre1 and reduced following the procedures of Evans et al. (2009) using the Swift software and calibration database available within HEASOFT v.6.12. Simultaneous observations were made with the Swift BAT (Barthelmy et al. 2005) at 14–195 keV and the Swift UV/Optical Telescope (UVOT; Roming et al. 2005) with the u filter applied. For ten sources, additional archival Swift observations were available at the time of writing which we have included and analysed in an identical manner. Details of all the observations used in this paper are given in the appendix (see Table A.1). With the XRT we detected 16 (or two-thirds) of the sample sources in our fill-in observations. Widening our search, we looked at data stacks in the Swift XRT Point Source Catalogue (1SXPS; Evans et al. 2014) and other pointed XRT observations and found that a further five sources were detected. Two of the three XRT non-detected sources have only ever been detected in the XMM slew survey (Figs. 1b and f), and cannot be identified with any source detected in other wavelength surveys such as 2MASS, WISE, SDSS or 6dF in our searches. One of these, XMMSL1 J015510.9-140028, lies at the detection threshold of the slew survey. The other, XMMSL1 J113001.8+020007, has a higher significance, however the photons at the source location are aligned along a row which indicates that this might not be an astrophysical point source. We conclude therefore that those two detections in the slew survey are highly likely to be spurious. We discuss the spurious fraction further in Sect. 8. The remaining XRT-undetected source is XMMSL1 J193439.3+490922, which has three detections in XMM slews and is hence likely real. All XRT-detected sources are also detected with UVOT. Three sources (Mrk 352, ESO 362-G018, ESO 139-G012) can be found in the Swift BAT 70-month All-Sky Hard X-ray Survey Source Catalog (Baumgartner et al. 2013).
Citation: Roming et al. 2005
Citation function: Uses; Uses; Uses
To calculate the boundaries of the HZ of a stagnant-lid Earth around M, K, G, and F dwarfs, we applied a 1D, cloud-free, radiative-convective climate model, which has been described in detail by von Paris et al. (2010) and von Paris et al. (2015), and is based on Kasting et al. (1984) and Segura et al. (2003). The radiative transfer is split into a stellar and a thermal wavelength regime. The short wavelength regime treats the absorption and scattering of stellar irradiation using a δ-two-stream method including Rayleigh scattering coefficients following the approach of Allen (1973) and four-term correlated-k exponential sums covering a wavelength regime from 273.5 nm to 4.545 μm. This wavelength coverage is optimized for solar irradiation. Especially for late M dwarfs the cut-off at 4.545 μm leads to non-negligible loss in incoming radiation of up to ≈5% (see also Wunderlich et al. 2019). Hence, HZ boundaries obtained with the models lie closer to the star than would be expected when accounting for this missing portion of irradiation. The long-wavelength regime treats the absorption by CO2 and H2O in the wavelength regime from 1 to 500 μm via correlated-ks computed based on HITEMP 1995 (Rothman et al. 1995). The ckd continuum (Clough et al. 1989), and the collision-induced absorption as described in Kasting et al. (1984) for CO2 and as described in von Paris et al. (2013) for N2 –N2 are included. Convection is treated by applying a convective adjustment when the adiabatic lapse exceeds the radiative lapse rate, including latent heat release from H2O or CO2 where applicable. The water mixing ratio profile (

$C_{\mathrm{H}_2\mathrm{O}}$



C


H
2

O



) is calculated from the temperature profile, the saturation vapour pressure (psat), and by assuming a relative humidity (RH): 

$C_{\mathrm{H}_2\mathrm{O}}=\textrm{RH}\frac{p_{\mathrm{sat}}}{p}$



C


H
2

O

=RH


p

sat


p



, with p the pressure of the atmosphere. By making use of our 1D climate model we estimate global diurnal mean values without accounting for effects such as slow planetary rotation or an interactive hydrological cycle. A discussion on the potential influence of 3D processes is given in Sect. 4.
Citation: Wunderlich et al. 2019
Citation function: Uses; Uses
Taking advantage of modern computer architecture, numerical simulations have become a powerful tool for investigating SNR dynamical evolution by means of testing various scenarios with specific purposes (Ferrand 2020). As relevant observational evidence is steadily growing, particular attention has been devoted to tackling with SNR temporal evolution in an inhomogeneous medium, aiming at deciphering the observed asymmetries of emission morphology or clarifying the effects on the synthetic radio polarization maps exerted by various configurations of the interstellar magnetic field (e.g., Orlando et al. 2007; Schneiter et al. 2015; Yang et al. 2015; Bandiera & Petruk 2016; Petruk et al. 2017). As a first attempt, magnetohydrodynamic (MHD) simulations of a benchmark type Ia SNR expanding into a turbulent background were carried out by Balsara et al. (2001), and several obtained results are different from those in a uniform medium: significant azimuthal variations observed in density and magnetic field profiles, a patchy and time-independent synchrotron shell which may in turn serve as a probe into the nature of ISM turbulence, and amplification of the magnetic field in the post-shock region due to interactions between the forward shock and the turbulent background. Under the assumption that both density and magnetic field fluctuations follow a Kolmogorov-like power spectrum, the temporal evolution of an SNR propagating into a turbulent medium was investigated by Guo et al. (2012), with a focus on the structures and amplification of the magnetic field in the shock downstream. Recently, two-dimensional cylindrical MHD simulations were implemented to investigate the dynamical properties of young type Ia SNRs undergoing shock acceleration in a turbulent medium by Peng et al. (2020), where an initial power-law density profile is adopted and the derived relative contact discontinuity positions are compared with the observed results of two typical type Ia SNRs: SN 1006 and Tycho.
Citation: Bandiera & Petruk 2016
Citation function: Motivation
The main RSG sample contains 1405 candidates from the SMC source catalog. However, targets with Rank 4 and 5 are selected only in one CMD by either the MIST models or the theoretical J − KS color cuts, and many of them reach down close to the tip of the red giant branch (TRGB) and AGB population (see also Figs. 13 and 14 of Yang et al. 2019). To be on the safe side we adopted only targets with ranks from 0 to 3 (targets identified in at least two CMDs) as our initial sample, which resulted in 1107 targets. Due to the photometric quality cuts and uncertainties of the Spitzer and Gaia data, the strict constraints on the astrometric solution, and the deblending applied during the construction of the source catalog, some of the spectroscopically confirmed RSGs were also rejected. In order to make the sample as complete as possible, we retrieved and added all known spectroscopic RSGs in both optical and mid-infrared (MIR) bands from Simbad (Wenger et al. 2000) and data taken by Spitzer Infrared Spectrograph (IRS; Houck et al. 2004), respectively. From Simbad, we selected 322 RSGs with RV ≥ 90 km s−1, spectral type later than G0, and luminosity class brighter than II by using criteria query (Levesque 2013; González-Fernández et al. 2015), for which 192 targets were matched with our initial sample within 1″. Additionally, a crossmatching with the main RSG sample of 1405 candidates indicated that three Rank 4 candidates were also matched within 1″. Surprisingly, there are two spectroscopic RSGs matched with the source catalog within 1″, but not selected as the RSG candidates by either the MIST models or the theoretical J − KS color cuts. Visual inspection of Gaia and 2MASS (Two Micron All Sky Survey; Skrutskie et al. 2006) CMDs (shown below) indicated that these two targets were slightly off the blue and red boundaries of the RSG region, respectively, which was likely due to the intrinsic variability of the RSGs (Kiss et al. 2006; Yang & Jiang 2011; Ren et al. 2019). Consequently, in total, there are 127 unselected spectroscopic RSGs from Simbad. For data taken by Spitzer/IRS, there were 22 RSGs from Ruffle et al. (2015), who classified 209 point sources observed by Spitzer/IRS using a decision tree method, based on IR spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership, and variability information (all the targets from Kraemer et al. 2017 were also included). Of these 22 RSGs, 16 of them were matched with our initial sample within 1″, and 4 of them were matched with the previous unselected Simbad RSGs within 1″. Thus, there are only two unselected spectroscopic RSGs from Spitzer/IRS. In total, there are additional 129 spectroscopic RSGs from both Simbad and Spitzer/IRS, for which we give them Rank −1.
Citation: Wenger et al. 2000
Citation function: Uses
Considering a BBH system resulting from a gas rich merger, the BBH is probably surrounded by a circumbinary disk, and each of the two SMBHs is associated with a mini-disk (see Figure 1). In between the circumbinary disk and the inner mini-disks, a gap (or hole) is opened by the secondary SMBH, which is probably the most distinct feature of a BBH–disk accretion system, in analogy to a system in which a gap or hole is opened by a planet migrating in the planetary disk around a star (Lin et al. 1996; Quanz et al. 2013). This type of geometric configurations for the BBH–disk accretion systems has been revealed by many numerical simulations and analysis (Artymowicz & Lubow 1996; Escala et al. 2005; Hayasaki et al. 2008; Cuadra et al. 2009; D’Orazio et al. 2013; Farris et al. 2014; Roedig et al. 2014).4

4
The width of the gap (or hole) is roughly determined by, but could be somewhat larger than, the Hill radius 





. However, set a slightly large gap size, e.g., 





, does not affect the results presented in this paper significantly.
 The continuum emission from disk accretion onto a BBH may be much more complicated than that from disk accretion onto a single SMBH, since the dynamical interaction between the BBH and the accretion flow onto it changes the disk structure (Gültekin & Miller 2012; Sesana et al. 2012; Rafikov 2013; Roedig et al. 2014; Yan et al. 2014; Farris et al. 2015). Nevertheless, we adopt a simple model to approximate the continuum emission from a BBH–disk accretion system as the combination of the emissions from an outer circumbinary disk and an inner mini-disk around the secondary SMBH, each approximated by multicolor blackbody radiation in the standard thin disk model (Novikov & Thorne 1973; Shakura & Sunyaev 1973). The emission from the mini-disk around the primary SMBH is insignificant for a BBH system with a small mass ratio (roughly in the range of a few percent to 0.25) due to its low accretion rate as suggested by the state of the art numerical simulations (Roedig et al. 2012; Farris et al. 2014), thus its emission can be neglected. Our analysis suggests that a large q cannot lead to a good fit to the observations.
Citation: Hayasaki et al. 2008
Citation function: Background
There is significant scatter (larger than 1 dex) among the tdepl measurements in all redshift bins, even though we only consider MS galaxies with ΔMS = ±0.3 dex around the MS parametrization of Speagle et al. (2014). This scatter at a fixed redshift is believed to be a product of the multi-functional dependence of tdepl on many physical parameters, such as the offset from the MS, the star formation rate, the stellar mass, and possibly the environment (e.g., Dessauges-Zavadsky et al. 2015; Scoville et al. 2017; Noble et al. 2017; Silverman et al. 2018; Tacconi et al. 2018; Tadaki et al. 2019; Liu et al. 2019b). Given the strong anti-correlation found between tdepl and the offset from the MS (Genzel et al. 2015; Dessauges-Zavadsky et al. 2015; Tacconi et al. 2018), we still expect tdepl variations for galaxies on the MS while in their evolutionary process they are transiting up and down across the MS band (e.g., Sargent et al. 2014; Tacchella et al. 2016). The previously reported anti-correlation between tdepl and sSFR (Saintonge et al. 2011; Dessauges-Zavadsky et al. 2015) is also further supported by our galaxies at z = 4.4 − 5.9 (Fig. 6, middle panel). This highlights comparable timescales for gas consumption and stellar mass formation. We find a Spearman rank coefficient of −0.49 and p-value of 4.5 × 10−10 for the dependence of tdepl on sSFR when considering the MS SFGs at z ∼ 1 − 5.9. The observed offset of ALPINE galaxies with respect to the tdepl–sSFR relation of MS SFGs at z = 0 and to a smaller extent to the relations at z ∼ 1 and z ∼ 2 is compatible with the displacement of the z = 0 relation along the sSFR-axis by factors derived from the sSFR evolution with redshift of MS SFGs out to z ∼ 5 (Speagle et al. 2014). Nevertheless, a less steep sSFR redshift evolution toward z ∼ 5 than parametrized by Speagle et al. (2014) is suggested by the ALPINE sample, in line with the sSFR(z) results of Khusanova et al. (2020a). On the other hand, with tdepl measurements achieved down to Mstars ∼ 108.4 M⊙ for the ALPINE galaxies, we confirm that for MS SFGs at z ∼ 1 − 5.9 the tdepl dependence on Mstars, if any, must be weak as shown in Fig. 6 (right panel). This further supports the idea that the linear KS relation established for local galaxies (Kennicutt 1998b) might hold up to z ∼ 5.9 for MS SFGs.
Citation: Saintonge et al. 2011
Citation function: Similarities; Similarities
The fact that all massive ETGs fall into a single FP, coupled with the segregation of the highest ranked objects from lesser galaxies, entails that any non-edge-on projection of this flat surface must also lead to segregated 2D scaling laws. To investigate this issue we now use M⋆ to represent scale, shifting to the (logarithmic) RVM coordinate system defined by the three most basic global parameters connected by the standard plane. In this manner we set the framework for the study of two of the most firmly established empirical scaling relations of elliptical galaxies: the Kormendy-like RM relation (Shen et al. 2003) and the VM relation, a mass analogue of the classical FJR. In good agreement with observational studies of BCG/BGGs (Bernardi et al. 2007; Lauer et al. 2007; Bernardi 2009; Méndez-Abreu et al. 2012), we find that our simulated first-ranked galaxies lie off the standard 2D scaling relations defined by the bulk of the ETG population. In particular, as shown in Fig. 3, BGGs are larger and have lower effective velocity dispersions than ordinary ellipticals of the same stellar mass. The combination of this latter result with the fact that light appears to be similarly concentrated for BGGs than for non-BGGs (Paper I), tells us that the total mass-to-light fraction interior to Re is lower for the former than for the latter. Our numerical experiments, however, do not seem to support claims of the steepening of the R ∝ Mα relation for CGs towards values α ≳ 1 (Lauer et al. 2007; Bernardi 2009). We find instead that our first-ranked objects, which occupy a locus moderately offset from the central axis of the observational data in Fig. 3A, are well fitted by a model with α = 0.60 ± 0.03. This value of the power-law index agrees well with the slope α ∼ 0.65 found for non-BCG galaxies in the Sloan r-band (Shen et al. 2003; Bernardi et al. 2007), while a plain orthogonal fit to the general elliptical population data included in Fig. 3A (green dots and small grey circles) also gives α ∼ 0.6. Approaching closer to our findings, Liu et al. (2008) obtain α ∼ 0.9 from surface measurements up an isophotal limit of 25 r-mag arcsec−2, which reduces to ∼0.8 when their magnitudes are transformed into mass in old stars, whereas they get α ∼ 0.75 for a control sample of non-BCGs. Differences in the photometry, the waveband of the observations, sample construction (i.e. incompletenesses and selection biases), and fitting methods would help explain the lack of a closer agreement between these results about the most robust of the 2D relationships.
Citation: Bernardi 2009
Citation function: Differences
Our results appear to stand apart from similar studies in full spherical shells (e.g., Nelson et al. 2013; Hotta et al. 2016) in that the differential rotation is strongly quenched as a function of the magnetic Reynolds number. However, in Nelson et al. (2013) the values of Rm′ (=2πReM) correspond to a range of 8...32 in ReM in our units where the radial and latitudinal differential rotation decrease by about 30 per cent. This is roughly consistent with our results. On the other hand, Hotta et al. (2016) reached higher values of ReM than in the present study, but no strong quenching was reported. The reason might be that their models are rotating substantially slower than ours, leading to weaker magnetic fields and a weaker back-reaction to the flow. Furthermore, in these models, the differential rotation is strongly influenced by their SGS heat flux, which transports one third of the luminosity. Another obvious candidate for explaining the difference is the wedge geometry used in the current simulations. However, we note that earlier simulations with a similar setup did not show a marked trend in the energy of the differential rotation as the azimuthal extent of the domain was varied (see Table 1 of Käpylä et al. 2013). However, results of Boussinesq simulations of convective dynamos have shown a similar change as a function of the magnetic Prandtl number (Schrinner et al. 2012). The drop in the amplitude of the differential rotation was associated with a change in the dynamo mode from an oscillatory multipolar solution to a quasi-steady dipolar configuration (cf. Fig. 15 of Schrinner et al. 2012) that prevents strong differential rotation from developing. We do not find a strong dipole component in our simulations (see Sect. 4.2.3). However, the strong suppression of the differential rotation often coincides with the appearance of a small-scale dynamo (see Table 1 and the discussion in the Sect. 4.1.2) or a change in the large-scale dynamo mode as discussed above.
Citation: Hotta et al. 2016
Citation function: Differences; Differences
As discussed in Section 1, one of the primary motivations for this work is to attempt to understand the variations in apparent IMF that have been observed in early type galaxies (ETGs). While there are multiple lines of evidence for this variation, the most straightforward to extract from out simulations is the mass to light ratio of the stellar populations we produce. In this section we therefore look at the mass to light ratio in our simulations for the purpose of comparing to that in observed galaxies. We calculate this by using the slug stellar population synthesis code (da Silva, Fumagalli & Krumholz 2012; Krumholz et al. 2015) to generate isochrones at stellar population ages from 5 Gyr to 10 Gyr, using the MIST stellar evolution tracks (Choi et al. 2016) and Starburst99-style stellar atmosphere models (Leitherer et al. 1999). Each isochrone provides a prediction of present-day mass, bolometric luminosity, and luminosity in a range of photometric filters as a function of initial mass for stars with initial mass $\ge 0.1\, {\rm M}_{\odot }$; we assume that the luminosities of stars with initial masses less than 0.1 M⊙ are negligible, and that these stars also experience negligible mass loss. We further assume that all stars with initial mass $\lt 8\, {\rm M}_{\odot }$ (which are all the stars formed in our simulations) that reach the end of their lives leave behind 0.7 M⊙ white dwarf remnants. We use the isochrone to calculate the luminosity and present-day mass of all the stars formed in each of our simulations at ages from 5 − 10 Gyr, and from these we calculate the mass to light ratio of the stellar population as a function of age for each of our simulations. For comparison, we use the same isochrones to calculate the mass to light ratio of Chabrier (2005) and Salpeter (1955, truncated at a lower mass limit of $0.1\, {\rm M}_{\odot }$) IMFs at the same ages. We use the SDSS r band for this calculation, but results are qualitatively similar in other filters.
Citation: Krumholz et al. 2015
Citation function: Uses