adsabs/FOCAL huggingface dataset:
We test our calibration procedure on the buzzard-v1.1 simulation, a mock DES Y1 survey created from a set of dark-matter-only simulations. The simulation and creation of the mock survey data are detailed in DeRose et al. (in preparation), Wechsler et al. (in preparation), and MacCrann et al. (2018), so we provide only a brief summary of both. buzzard-v1.1 is constructed from a set of three N-body simulations run using l-gadget2, a version of gadget2 (Springel 2005) modified for memory efficiency. The simulation boxes ranged from 1 to 4 Gpc h−1. Light cones from each box were constructed on the fly. Haloes were identified using rockstar (Behroozi, Wechsler & Wu 2013), and galaxies were added to the simulations using the Adding Density Dependent GAlaxies to Light-cone Simulations (addgals) algorithm (Wechsler et al., in preparation). addgals uses the large-scale dark matter density field to place galaxies in the simulation based on the probabilistic relation between density and galaxy magnitude. The latter is calibrated from subhalo abundance matching in high-resolution N-body simulations. SEDs are assigned to the galaxies from a training set of spectroscopic data from SDSS Data Release 7 (DR7; Cooper et al. 2011) based on local environmental density. The SEDs are integrated in the DES pass bands to generate griz magnitudes. Galaxy sizes and ellipticities are drawn from distributions fit to deep SuprimeCam $i^{{\prime }}$-band data. Galaxies are added to the simulation to the projected apparent magnitude limit of DES Year 5 (Y5) data out to redshift z = 2. The galaxy positions, shapes and magnitudes are then lensed using the multiple-plane ray-tracing code Curved-sky grAvitational Lensing for Cosmological Light conE simulatioNS (calclens; Becker 2013). Finally, the catalogue is cut to the DES Y1 footprint with RA > 0 using the footprint and bad region masking including bright stars, regions of high extinction, etc., used in the actual Y1 data, and photometric errors are added using the DES Y1 depth map (Rykoff, Rozo & Keisler 2015). This yields a total masked area of 1108.13 deg2, 12 million WL source galaxies, and 102 120 galaxies in the higher luminosity redMaGiC sample used in this paper, as will be discussed in Sections 3.2 and 3.3.
Citation: Behroozi, Wechsler & Wu 2013
Citation function: Uses
There has additionally been the concern that the optical light curves do not look as expected if they arise from reprocessing of X-ray emission from a small central corona, e.g. of size similar to that which we measure from microlensing observations, i.e. $\rm{\, \stackrel{\lt }{_\sim }\,}10 R_{G}$ (Dai et al. 2010; Mosquera et al. 2013), or from X-ray low/high energy reverberation, i.e. ∼4Rg (Cackett et al. 2014; Emmanoulopoulos et al. 2014). The observed optical light curves are smoother than expected and an insufficient fraction of the X-ray emission hits the disc to power the optical variability (e.g. Berkley, Kazanas & Ozik 2000; Arévalo et al. 2008). Larger coronal sizes are required. Gaskell (2008) also notes the energetics problem and proposes variations originating independently in different parts of the disc. However, although such a model is useful for explaining the uncorrelated variations between bands which are sometimes seen, it cannot explain the well correlated multiwavelength variations seen in the Swift observations. Gardner & Done (2017) proposed an alternative model in which the X-ray emission does not directly impact on the outer disc but mainly heats up the very inner edge of the disc, which then inflates and re-radiates at hard UV wavelengths on to the outer disc. In this model there should be an additional lag between the X-ray and UVW2 emission, over and above that expected from an extrapolation of the longer wavelength lags down to the X-ray waveband. This additional lag would correspond to the thermal time-scale for the incident X-ray heating to pass through the inner disc to the re-radiation surface. Gardner & Done (2017) note the existence of such a lag when the unfiltered X-ray and UVW2 observations of NGC 5548 are compared. However, McHardy et al. (2014) do not see any additional lag in NGC 5548 if those light curves are filtered to remove variations on time-scales longer than 20 d. In NGC 4151, Edelson et al. (2017) see a very large excess lag between the X-ray and UVW2 bands. Unlike in NGC 5548, the excess lag in NGC 4151 is strongly energy dependent, with the highest energy X-rays having the largest lag. NGC 4151 is the most absorbed of the few AGN whose lags have been well studied so far and so the energy dependence may be a function of scattering in the absorbing medium.
Citation: Berkley, Kazanas & Ozik 2000
Citation function: Compare/Contrast
To determine the median spectral index and the variation within the sample we perform a bootstrap analysis, randomly drawing from our sample (with replacement) and median stacking both the LoTSS and FIRST cutouts independently to obtain a median peak pixel flux density measurement in each survey. This is then repeated 10 000 times to produce a distribution that also incorporates uncertainties due to sample variation. The 16th and 84th percentile of the resulting peak flux density distributions, together with the systematic flux scale density uncertainty of 5% (White et al. 1997) and 10% (Shimwell et al., in prep.) for the FIRST and LoTSS survey, respectively, are used to determine the error on the spectral index. This stacking procedure is done for all 93 quasars that overlap with FIRST (red) and for only the 35 sources detected by LoTSS with S/N > 2 that overlap with FIRST (grey), resulting in spectral indices of 



−
0
.

29

−
0.09


+
0.10




$ -0.29^{+0.10}_{-0.09} $


 and 



−
0
.

24

−
0.22


+
0.09




$ -0.24^{+0.09}_{-0.22} $


, respectively. Six of the FIRST detected quasars have spectral indices higher than these stacked values, which is as expected because these flat or positive spectral index quasars are naturally selected because of the FIRST flux limit of ∼144 μJy, which is shallower than the LOFAR flux limit assuming a negative spectral index. In general, LOFAR detected quasars are brighter at 144 MHz than the non-detected ones, therefore one might expect the stacked S/N > 2 sample to result in a steeper spectral index than when stacking all quasars. However, given the uncertainties on the median spectral index there is no significant deviation found between the two. In this work, we therefore assume a spectral index of −0.29±0.10 for the quasars not detected by FIRST when converting radio fluxes to luminosities. This spectral index is in line with the previous work of Gürkan et al. (2019), who found a median spectral index of −0.26 ± 0.02 for a large sample of optically selected quasars at z ≲ 3.
Citation: White et al. 1997
Citation function: Uses; Uses
The lack of data on inelastic processes due to collisions with neutral hydrogen atoms has been a major limitation on modelling of F-, G- and K-star spectra in statistical equilibrium, and thus to reliably proceeding beyond the assumption of local thermodynamic equilibrium (LTE) in analysis of stellar spectra and the determination of elemental abundances. This problem has been well documented, e.g. see Lambert (1993); Barklem (2016a) and references therein. Significant progress has been made in recent times through detailed full-quantum scattering calculations, based on quantum chemical data, for the cases of simple atoms such as Li, Na, Mg and Ca (Belyaev & Barklem 2003; Barklem, Belyaev & Asplund 2003; Belyaev et al. 2010; Barklem et al. 2010; Belyaev et al. 2012; Barklem et al. 2012; Mitrushchenkov et al. 2017). These calculations have demonstrated the importance of the ionic-covalent curve crossing mechanism leading naturally to charge transfer processes (mutual neutralization and ion-pair production), in addition to excitation and de-excitation processes. The importance of this mechanism has allowed various simplified model approaches to be developed, which may be used in cases where suitable quantum chemistry data are not been available. In particular a semi-empirical model has been employed for Al, Si, Be and Ca (Belyaev 2013a,b; Belyaev, Yakovleva & Barklem 2014b; Yakovleva, Voronov & Belyaev 2016; Belyaev et al. 2016), and a theoretical model based on a two-electron asymptotic linear combinations of atomic orbitals (LCAO) approach, has also been employed for Ca (Barklem 2016b, 2017). Comparisons of the two methods show quite good agreement and reasonable agreement with the full quantum calculations is found, particularly for the most important processes with the largest rates (Barklem 2016b, 2017; Mashonkina, Sitnova & Belyaev 2017; Mitrushchenkov et al. 2017). Thus, the model approaches provide a useful route for obtaining estimates of the rates for these processes for many elements of astrophysical interest.
Citation: Mitrushchenkov et al. 2017
Citation function: Similarities
Citation: Mitrushchenkov et al. 2017
Citation function: Background
Citation: Mitrushchenkov et al. 2017
Citation function: Similarities
Citation: Mitrushchenkov et al. 2017
Citation function: Background
The E-MOSAICS project (Pfeffer et al. 2018; Kruijssen et al. 2019a) is a suite of cosmological, hydrodynamical simulations of galaxy formation in the Λ cold dark matter cosmogony that couples the MOSAICS model for star cluster formation and evolution (Kruijssen et al. 2011; Pfeffer et al. 2018) to the EAGLE model of galaxy formation and evolution (Crain et al. 2015; Schaye et al. 2015). The simulations are run with a highly modified version of the N-body, smoothed particle hydrodynamics code gadget3 (last described by Springel 2005). Bound galaxies (subhaloes) were identified within the simulations using the subfind algorithm (Springel et al. 2001; Dolag et al. 2009), in the same manner as in the EAGLE simulations (for details see Schaye et al. 2015). EAGLE includes subgrid routines describing radiative cooling (Wiersma, Schaye & Smith 2009a), star formation (Schaye & Dalla Vecchia 2008), stellar evolution, and mass-loss (Wiersma et al. 2009b), the seeding and growth of black holes (BHs) via gas accretion and BH–BH mergers (Rosas-Guevara et al. 2015), and feedback associated with star formation and BH growth (Booth & Schaye 2009). As current cosmological simulations lack the resolution and physics necessary to compute the feedback efficiencies from first principles, the stellar and active galactic nuclei feedback parameters are calibrated such that the simulations of cosmologically representative volumes reproduce the galaxy stellar mass function, galaxy sizes, and BH masses at z ≈ 0. The EAGLE simulations successfully reproduce a range of galaxy properties, including the stellar masses (Furlong et al. 2015) and sizes (Furlong et al. 2017) of galaxies, their luminosities and colours (Trayford et al. 2015), their cold gas properties (Lagos et al. 2015, 2016; Bahé et al. 2016; Marasco et al. 2016; Crain et al. 2017), and the properties of circumgalactic and intergalactic absorption systems (Rahmati et al. 2015, 2016; Oppenheimer et al. 2016; Turner et al. 2016, 2017). The simulations also largely reproduce the cosmic SFR density and relation between specific SFR and galaxy mass (Furlong et al. 2015). The simulations are therefore ideal for comparisons with observed galaxy populations.
Citation: Dolag et al. 2009
Citation function: Uses; Similarities
The detailed kinematic reconstruction of the Sgr tidal debris by Law & Majewski (2010) used an initial total mass of 





 M⊙ for the Sgr satellite. However, several studies point to a Sgr remnant mass significantly exceeding that value. Ibata et al. (1997) and Ibata & Lewis (1998) estimate lower bounds of 109 M⊙ for the mass of the dwarf today. With the discovery of previously unseen branches of the stream, the total luminosity budget of the progenitor galaxy is now believed to be on the order of 108 L⊙ (Niederste-Ostholt et al. 2010). As a result, recent studies have shifted to using dark-matter halo masses as large as 1011 M⊙ (Purcell et al. 2011; Gòmez et al. 2015) based on halo abundance matching arguments. Such high values are comparable to the mass of the LMC progenitor (see e.g., Jethwa et al. 2016; Peñarrubia et al. 2016) and imply a mass ratio relative to the MW on the order of 1:10. However, unlike the Magellanic Clouds, which may be on their first passage near the MW (Besla et al. 2007), Sgr is known to have experienced multiple close passages in the past. If true, such high Sgr progenitor masses would have important implications for the formation and evolution of the MW disk (e.g., Purcell et al. 2011; Gòmez et al. 2013; D’Onghia et al. 2016). Because the dynamical friction force is proportional to the square of the satellite mass, we expect drag to play a much more important role in slowing down the Sgr satellite and bringing it to closer galactocentric distances. Here, we perform an exploration across orbital angular momentum parameter space analogous to Section 3.1, this time using a Sgr progenitor mass of 6×1010 M⊙ according to the recent estimates of Gibbons et al. (2017). We consider two possibilities: a “slow sinking” scenario in which, as in Section 3, Sgr crosses the MW virial radius at 





 (approximately 8 Gyr ago), and a “rapid sinking” scenario, in which we examine a first infall around 





, about 4 Gyr ago.
Citation: Purcell et al. 2011
Citation function: Background; Background
A tidal disruption event (TDE) is an astronomical phenomenon that occurs when a star gets too close to a supermassive black hole in the galaxy center and is disrupted by the tidal force of the black hole. Part of stellar material is bound and accreted by the central black hole, resulting in bright optical, UV, and soft X-ray emission (Rees 1988; Lodato et al. 2015 and references therein). There are a growing number of candidate TDEs being discovered in soft X-ray, ultraviolet, and optical surveys; see Komossa (2015) for a recent review. Recently, three unusual TDE candidates have been discovered by Swift, i.e., Swift J164449.3+573451, Swift J2058.4+0516, and Swift J1112.2-8238 (hereafter Sw J1644+57, Sw J2058+05, and Sw J1112-82 for short, respectively), which have very bright nonthermal hard X-ray and radio emissions (Bloom et al. 2011; Burrows et al. 2011; Krimm et al. 2011a, 2011b; Zauderer et al. 2011; Cenko et al. 2012; Brown et al. 2015). The luminous nonthermal X-ray and radio emissions are thought to be produced by relativistic jets (Bloom et al. 2011; Burrows et al. 2011; Levan et al. 2011; Zauderer et al. 2011; Cao & Wang 2012; Metzger et al. 2012; Wang et al. 2014; Liu et al. 2015). Sw J1644+57 shows a highly variable light curve in X-rays, as observed by the X-ray Telescope on board Swift. At redshift 





, the isotropic luminosity of the X-ray emission is as high as 





 erg s−1. Sw J2058+05 exhibits a luminous, long-lived X-ray outburst with an isotropic peak luminosity of 





 (at redshift 





). Its total isotropic energy (0.3–10 keV) on a timescale of the first 2 months amounts to 1054 erg. Sw J1112-82 was initially also discovered by Swift/BAT (Burst Alert Telescope) in 2011 June as an unknown, long-lived (order of days) γ-ray transient source. It exhibits a similar bright X-ray flare, and its position is consistent with the nucleus of a faint galaxy at 





 (Brown et al. 2015). The peak X/γ-ray luminosity of Sw J1112-82 exceeds 





.
Citation: Krimm et al. 2011a
Citation function: Motivation
Kennicutt & Evans (2012) present a compilation of disk-averaged SFR and gas mass surface densities whose values have been calculated in a uniform manner across different galaxy types (including normal disk galaxies and dusty starburst galaxies selected in the IR) and find a power-law index of n ∼ 1.4. However, this result may be an artifact of combining galaxies of different interaction states. For a sample of z ∼ 1–3 MS galaxies, Tacconi et al. (2013) find an index consistent with unity and only a slight offset between their high-redshift sample and a low-redshift sample with similar masses. However, SMGs and other ultra-/luminous infrared galaxies (U/LIRGs) are further offset above the correlation for star-forming disk galaxies even when similar CO-to-H2 conversion factors are used for all galaxy populations (see also Bigiel et al. 2008; Daddi et al. 2010b; Genzel et al. 2010, 2015; Tacconi et al. 2018). In analyses of the resolved star formation properties of nearby disks, a near-unity index for the Schmidt–Kennicutt relation is also found in regimes where the molecular gas dominates the total gas mass surface density (Σgas >9 M⊙ pc−2; e.g., Bigiel et al. 2008, 2010; Schruba et al. 2011). The surface density version of the Schmidt–Kennicutt relation has been evaluated within only eight high-redshift galaxies: SMM J14011+0252 at z = 2.56 (Sharon et al. 2013), EGS 13011166 at z = 1.53 (Genzel et al. 2013), HLS 0918 at z = 5.24 (Rawle et al. 2014), GN20 at z = 4.05 (Hodge et al. 2015), PLCK G244.8+54.9 at z = 3.00 (Cañameras et al. 2017), AzTEC-1 at z = 4.34 (Tadaki et al. 2018), and the two components of HATLAS J084933 at z = 2.41 (Gómez et al. 2018).9

9
Freundlich et al. (2013) and Sharda et al. (2018) also examine the Schmidt–Kennicutt relation at z > 1, but they analyze individually resolved clumps within high-redshift galaxies rather than performing full pixel-by-pixel comparisons.
 These studies find a range of Schmidt–Kennicutt relation indices (n = 1–2). It is particularly worth noting that Genzel et al. (2013) find that their measured index depends strongly on which spatially resolved extinction correction they apply to their Hα measurements.
Citation: Bigiel et al.; 2010
Citation function: Similarities
The detection criterion of having the full interval α ± σα inside the prior range [0.8, 1.2] (e.g., Ata et al. 2018; Chan et al. 2018) has commonly been employed in the literature. However, we can observe from these analyses that the distribution of the α parameter in general seems to be more Gaussian than what we find in our analyses (left panels in Figs. 3 and 4). A possible reason for this is the lower redshift we considered here, where the contribution from nonlinear effects is stronger. However, results from Villaescusa-Navarro et al. (2017), who investigated the BAO detection from 21 cm signal for the SKA case for the redshift range 0.35  z  3.05, also showed α distributions with clear deviations from a perfect Gaussian (but note the smaller number of simulations employed there, namely, 100). As pointed out by Chan et al. (2018) and Abbott et al. (2022), a natural consequence from having a (approximate) Gaussian distribution is a reasonable concordance among the three different error measurements, that is, ⟨σα⟩∼σ68 ∼ σstd. This is not our case, as can be seen from Tables 2 and 3 (as well as from Tables 4 and 5), indicating that ⟨σα⟩ is not meaningful or representative of the error in the α measurements for individual mock realizations (see also Figs. 3 and 4). Our results show that compared to ⟨σα⟩, the errors given by σ68 are overestimated by ∼18% to 33%, from the smallest to the highest z-bins when the ACF is used, but is underestimated by ∼50% for APS. Moreover, comparing the expected 




σ
α
m



$ \sigma^m_\alpha $



 obtained fitting the mean Cℓ and ω(θ) to the average ⟨σα⟩, we find a reasonable agreement for intermediate and higher z-bins, but not for the lower z-bins, in particular, for the ACF estimator. In addition, although σstd agrees better with σ68, we still find non-negligible differences among them, which confirms our α distributions as non-Gaussian (a larger σstd indicates non-Gaussian tails). These reasons motivated our choice of using the α values instead of α ± σα ranges, which belong to the interval [0.8, 1.2] as a criterion for a BAO detection (defining the Nd fraction), as well as our choice of using the 68% spread of the α distributions, σ68, as the representative error in our measurements.
Citation: Chan et al. (2018)
Citation function: Compare/Contrast; Compare/Contrast
In order to investigate the gas kinematics at an early evolutionary stage and the formation of Keplerian disks, we conduct ALMA observations toward three candidate young protostars, Lupus 3 MMS, IRAS 15398−3559, and IRAS 16253−2429. They are selected from our SMA sample (Yen et al. 2015a). These three protostars all have relatively low protostellar masses (0.1 M⊙), inferred from the infalling motions in their protostellar envelopes, and they do not show clear signs of a spin-up rotation on a 1000 au scale; i.e., no signatures of Keplerian disks are seen in our SMA observations (Yen et al. 2015a). Lupus 3 MMS is a Class 0 protostar with a bolometric luminosity (Lbol) of 0.41 L⊙ and a bolometric temperature (Tbol) of 39 K in the Lupus 3 cloud at a distance of 200 pc (Tachihara et al. 2007; Comerón 2008; Dunham et al. 2013). Our SMA results suggest that the protostellar mass in Lupus 3 MMS can be as low as 0.1 M⊙ (Yen et al. 2015a). IRAS 15398−3559 is a Class 0/I protostar with an Lbol of 1.2 L⊙ and a Tbol of 61 K in the Lupus 1 cloud at a distance of 150 pc (Froebrich 2005; Comerón 2008). Early single-dish observations of its CO outflow suggest that IRAS 15398−3559 is close to face on (van Kempen et al. 2009). Recent SMA and ALMA observations show that it is actually closer to edge on (Oya et al. 2014; Bjerkeli et al. 2016). With this new estimated inclination angle (∼70°), our SMA data suggest a low protostellar mass (0.1 M⊙) and a low specific angular momentum in the protostellar envelope (∼1 × 10−4 km s−1 pc; Yen et al. 2015a). IRAS 16253−2429 is a Class 0 protostar with an Lbol of 0.24 L⊙ and a Tbol of 36 K in the ρ Ophiuchus star-forming region at a distance of 125 pc (Dunham et al. 2013). Both CARMA and our SMA results suggest that its protostellar mass is 0.1 M⊙ (Tobin et al. 2012a; Yen et al. 2015a). These three protostars are all embedded in dense cores with masses ≳0.5 M⊙ (Froebrich 2005; Tachihara et al. 2007; Enoch et al. 2009). Therefore, they are excellent targets by which to study the gas motions on a 100 au scale at an early evolutionary stage.
Citation: Tachihara et al. 2007
Citation function: Motivation; Motivation
Citation: Tachihara et al. 2007
Citation function: Background