adsabs/FOCAL huggingface dataset:
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.
Citation: Gustafsson et al. 2008
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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).
Citation: Lu et al. 2013
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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.
Citation: Kelson et al. 2000
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Citation: Jørgensen 1999
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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).
Citation: Tomida et al. 2013
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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).
Citation: Susa 2013
Citation function: Motivation; Motivation
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).
Citation: Scoville et al. 2014
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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.
Citation: Bergin & Tafalla (2007)
Citation function: Uses
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.
Citation: Soleri & Fender 2011
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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.
Citation: Huang et al. 2018a
Citation function: Uses
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).
Citation: Tempel et al. (2014a)
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