adsabs/FOCAL huggingface dataset:
Figure 1 shows the NGC 419 CMD after field-star decontamination. The cluster has an eMSTO at 20 ≤ V ≤ 22 mag. We adopted the Padova stellar evolution models (PARSEC CMD 2.7, v. 1.2S; Bressan et al. 2012),8

8

http://stev.oapd.inaf.it/cgi-bin/cmd_2.7

 for Z = 0.004 (Glatt et al. 2008; Girardi et al. 2009), and fitted the eMSTO extremes with isochrones of log(t yr−1) = 9.12 (1.32 Gyr; blue line) and log(t yr−1) = 9.31 (2.02 Gyr; red line), adopting a distance modulus (m − M)0 = 18.90 mag and a visual extinction AV = 0.181 mag (Rubele et al. 2010). The maximum possible age spread implied by the extent of the eMSTO is ∼700 Myr, similar to the results of Rubele et al. (2010; ∼700 Myr) and Girardi et al. (2013; ∼670 Myr). However, the cluster exhibits an SGB that is broad on the blue side and that becomes significantly narrower on the red side. Previous studies of intermediate-age star clusters with eMSTOs usually showed tight SGBs throughout (Mackey & Broby Nielsen 2007; Li et al. 2014b; Bastian & Niederhofer 2015; but see Goudfrooij et al. 2015). This feature is, however, already apparent in the NGC 419 data before field-star decontamination and is, hence, not caused by our data reduction. In addition, the significance level of our field-star decontamination is very high along the SGB, while the good agreement between our iraf/daophot and dolphot stellar catalogs further confirms the reality of the SGB morphology. Examination of the SGB stars’ spatial distribution reveals that blending is unlikely responsible for the observed narrowing either. The NGC 419 CMD of Goudfrooij et al. (2014) shows a similar trend, although that of Glatt et al. (2008) resembles an SSP more closely. The latter CMD is, however, composed of HST ACS/High Resolution Channel observations covering a small stellar sample located in the cluster’s central region only. Our data are consistent with the Glatt et al. (2008) CMD for stars drawn from inside the cluster’s core radius. We thus conclude that the observed narrowing of the NGC 419 SGB is real and not caused by artifacts associated with our data reduction (see the Appendix).
Citation: Goudfrooij et al. 2015
Citation function: Differences; Differences
Our approach shares its transdimensional nature with that of Brewer et al. (2015), which implemented an independent transdimensional framework to infer substructure in strong lens systems. However, in addition to differences concerning substructure modeling and the sampling method used, our code and analysis were developed independently of theirs, and hence provide an important cross-check of the method and results. It has become customary in cosmological analyses to have at least two publicly available independent codes to check for numerical accuracy and find bugs. While both codes are public, we understand that this approach is not yet ubiquitous in the field of galaxy-scale strong lensing (for instance, it has been difficult thus far to reproduce/check the results of Vegetti et al. 2014 and Hezaveh et al. 2016), but we hope that our work can help make substructure lensing more reproducible. Given the uniqueness of the constraints it can provide on low-mass substructures and the impact that these detections can have on dark matter physics, we believe it is extremely important to have multiple cross-checks to validate any results. On a more technical level, our approach differs from that of Brewer et al. (2015) in two major ways:

1.
Subhalo lens modeling. We represent subhalos with NFW profiles with a certain mass, scale, and cutoff radius, whereas Brewer et al. (2015) uses so-called blobs, i.e., deflection as a power law in the subhalo-centric radius. We think that choosing a physically motivated basis function set to represent the deflection field due to subhalos is essential for correctly marginalizing them out, i.e., propagating their (rather large) uncertainties to the parameter of interests such as the subhalo mass function.


2.
The sampling method and the sampler used to sample from the posterior probability distribution of the (different) lens models given the data. Brewer et al. (2015) uses Diffusive Nested Sampling (DNS) as implemented in RJObject. We use a homebrew reversible-jump MCMC sampler with Metropolis-type within-model proposals.
Citation: Vegetti et al. 2014
Citation function: Differences; Differences
Our model consists of four zones; an inner disk (zone 1, as in Matter et al. 2016), and three zones into which the outer disk is divided (zones 2–4) in order to produce the azimuthal asymmetries seen in our SPHERE observations. The radial extent of these zones were constrained from our images: zone 2 corresponds to the component inside ~14 au observed in the noncoronagraphic J-band image, zone 3 corresponds to the bright ring at 16 au, and zone 4 corresponds to the asymmetrical outer disk. The radial extent of each of these zones, as well as their disk masses, is summarized in Table 2. No gaps were introduced between the three outer disk zones, but the gap between zones 1 and 2 from the Matter et al. (2016) model was kept. We note that these radii are only loosely constrained from our data, and are determined by eye from both the coronographic and noncoronographic data. We ran a grid of models with different inclinations and position angles for zones 1–2 (note that we systematically use the same inclination and position angle for these two zones) and for zone 3 – that is, a total offour free parameters, keeping in mind that the relative inclination between components must be small to allow a single broad shadow to be cast (as opposed to two narrow shadow lanes). The inclination and position angle of zone 4 is set to the values provided in Sect. 3. The grid was sampled in steps of 1° for inclination, and 2° for PA, and later refined to 0.5° steps for inclination and 0.5° for PA after a good initial agreement is found between the average azimuthal profile of the model and the scattered light images. The best fitting model was picked not only based on the location of the shadows cast by the inner components on the outer disk, but also on the shape (slope) of the resulting azimuthal profile of the outer disk. There is also a degeneracy if we consider that the inclinations and PAs of the two inner components (zones 1–2 and zone 3) can be exchanged and produce very similar results. However, doing this would cast a shadow in a different location, and thus produce a different azimuthal profile for zone 3.
Citation: Matter et al. 2016
Citation function: Uses
Kepler transit observations have shown that planets with sizes between those of Earth (1 R
⊕) and Neptune (∼4 R
⊕) are extremely common (Lissauer et al. 2011; Batalha et al. 2013; Fressin et al. 2013; Howard 2013; Fabrycky et al. 2014; Marcy et al. 2014). Demographics analysis suggests that at least 30%–55% of the Sun-like stars host one or more planets within this size range and with orbital periods shorter than 100 days (Mayor et al. 2011; Howard et al. 2012; Fressin et al. 2013; Petigura et al. 2013; Mulders 2018; Mulders et al. 2018; Zhu et al. 2018; He et al. 2019, 2021). Uncertainties in stellar radius estimates from photometric Kepler observations prevented a detailed assessment of the intrinsic planet size distribution (Fulton et al. 2017; Petigura et al. 2017). Specific trends in planet sizes only started to emerge from the data with more precise determination of stellar radii by follow-up surveys (California Kepler Survey, CKS) and the use of Gaia improved parallaxes (Johnson et al. 2017; Van Eylen et al. 2018; Petigura et al. 2022). These studies showed that the size frequency distribution of planets between ∼1 and ∼4R
⊕ is bimodal with peaks at ∼1.4 R
⊕ and ∼2.4 R
⊕, and a valley at ∼1.8 R
⊕ (Fulton et al. 2017; Fulton & Petigura 2018; Petigura 2020). The best-characterized planets with sizes of about ∼1.4 R
⊕ are consistent with rocky composition, as constrained by their estimated bulk densities (Fortney et al. 2007; Adams et al. 2008; Lopez & Fortney 2014; Weiss & Marcy 2014; Dorn et al. 2015; Wolfgang et al. 2016; Zeng et al. 2016, 2019; Bashi et al. 2017; Chen & Kipping 2017; Otegi et al. 2020). These planets are usually referred to as “super-Earths.” Planets with sizes of about ∼2.4R
⊕ are consistent with the presence of volatiles—which could reflect either rocky cores with H-He-rich atmospheres or ice/water-rich planets (Kuchner 2003; Rogers & Seager 2010; Lopez & Fortney 2014; Zeng et al. 2019; Mousis et al. 2020; Otegi et al. 2020). These planets are commonly referred to as “mini-Neptunes.” For a detailed discussion, see reviews by Bean et al. (2021) and Weiss et al. (2022), and references therein.
Citation: Petigura et al. 2017
Citation function: Background
In recent years, the Atacama Large Millimeter/submillimeter Array (ALMA) has carried out comprehensive surveys of the [C II] 158 μm fine structure line in high-z quasars. For example, Decarli et al. (2018) detected [C II] in 85% of 27 optically selected quasars at z > 5.94. This line is an important coolant that traces the ionized and neutral interstellar medium (ISM) and star-forming activities (Herrera-Camus et al. 2015). The detection of [C II] line and dust continuum emission has revealed a wide range of star formation rates, from a few 10 M⊙ yr−1 to ≥1000 M⊙ yr−1 (Decarli et al. 2018; Izumi et al. 2018). The [C II]–FIR luminosity ratios of these quasar hosts range from 10−4 to a few ×10−3 (Maiolino et al. 2005; Wang et al. 2013; Willott et al. 2017; Decarli et al. 2018), following the trend of decreasing [C II]–FIR ratio with increasing FIR luminosities found with the IR luminous star-forming systems (Malhotra et al. 2001; Luhman et al. 2003; Hailey-Dunsheath et al. 2010; Stacey et al. 2010; Graciá-Carpio et al. 2011; Díaz-Santos et al. 2013, 2017; Muñoz & Oh 2016; Smith et al. 2017; Decarli et al. 2018; Gullberg et al. 2018; Rybak et al. 2019). In particular, the quasar hosts with high FIR luminosities of a few 1012 to 1013 L⊙ show low [C II]–FIR ratios similar to that found in the ultraluminous infrared galaxies (ULIRGs) and submillimeter galaxies (SMGs; Luhman et al. 2003; Díaz-Santos et al. 2013, 2017; Wang et al. 2013; Rybak et al. 2019). The [C II]–FIR ratio is suggested to be related to the local conditions of the ISM; e.g., a decrease of [C II]–FIR ratio could be due to a high gas temperature (i.e., T > ΔE/k ∼ 91 K, ΔE is the energy separation of the two levels of the [C II]158 μm transition) where the upper level of the [C II] transition is saturated (Muñoz & Oh 2016), or a high gas surface density (e.g., the typical density in compact ULIRGs and SMGs) where more gas is in the molecular phase (Narayanan & Krumholz 2017). It is interesting to see how the resolved distributions of the line and continuum surface brightnesses and emission ratios in these FIR luminous quasar hosts compare to that in the ULIRGs and SMGs (Smith et al. 2017; Gullberg et al. 2018; Rybak et al. 2019).
Citation: Willott et al. 2017
Citation function: Compare/Contrast; Compare/Contrast
In the second, complimentary method, the line profiles were fitted using the least-squares method with the SCIPY routine CURVE_FIT. Toward B335, the fit was allowed to include up to four Gaussian line profiles, one for each potential transition in the blended emission feature. Bounds on the fits were estimated from the HDO data, and other non-blended emission lines in the dataset. The best fit does not include a fourth component, corresponding to either the CH3OCH3 line at 316.7904 GHz or the CH3OD transition at 316.7916 GHz (i.e., only three Gaussian profiles were included in the final fit). The exclusion of a fourth component has limited impact on the Gaussian profile for D2O. The resulting fit is shown the right panel of Fig. 4. The column density is estimated from the fitted line profile by assuming local thermodynamic equilibrium (LTE) (Mangum & Shirley 2015). We adopted an excitation temperature of 220 K, which is derived from the synthetic spectrum; this method results in a D2O column density of 4.0 × 1015 cm−2. The HDO transitions presented in Jensen et al. (2019) give a HDO column density of 1.9 × 1017 cm−2 at the sameexcitation temperature, when a beam filling factor is applied for a source size of 0.′′ 2. This method then results in a D2O/HDO ratio of (2.1 ± 0.6) × 10−2. Evidently, the D2O/HDO ratios toward B335 are somewhat higher when using the Gaussian fits, however both methods yield high D2O/HDO ratios ≳10−2. We note that the HDO column density is different from that presented in Jensen et al. (2019) because a higher excitation temperature of 220 K and a smaller source size are used in this work. Furthermore, the synthetic model includes an optical depth correction and identifies a slight blending of the H

$_{2}^{18}$218
O line, increasing the HDO/H2O ratio. Because of this, we consider the column densities presented in this work as more accurate. However, this does not impact the results and discussions of the relative HDO/H2O abundances in Jensen et al. (2019), in which identical excitation temperatures were used for all sources. The differences in the reported HDO/H2O ratios in this work and in Jensen et al. (2019) illustrate the dependence on the methodology and assumptions, however, the observed dichotomy between isolated and clustered protostars reported in that paper is robust. To remove the observed dichotomy requires consistently adopting lower excitation temperatures for the isolated sources, independent of the luminosity of the respective sources.
Citation: Mangum & Shirley 2015
Citation function: Uses
This scenario could also explain the reason why star-forming galaxies with a relatively thin disk appeared around z ∼ 1, because the hot-mode accretion is expected not to dominate even in massive halos at z ≳ 2 owing to gas supply through the cold gas stream (e.g., Dekel et al. 2009a; Kereš et al. 2009). Some of the gas accreting onto a dark matter halo is expected to penetrate surrounding hot gas in a form of filaments of dense and cold infalling gas and directly accrete onto the central galaxy at such high redshift, where the mass accretion rate and matter density tend to be high. Such direct gas supply through the filaments of cold gas could make the gas disk of the central galaxy more turbulent and gravitationally unstable, which leads to thick and clumpy stellar disk and bulge formation/growth in some cases (e.g., Dekel et al. 2009b; Ceverino et al. 2010; Dekel & Burkert 2014). Thus, it seems to be difficult to form the thin stellar disks even in massive halos at z ≳ 2. After the hot-mode accretion starts to dominate at z ∼ 2 in relatively massive halos, the thin stellar disks may be gradually formed from thinner gas disks and appear around z ∼ 1. If some of these star-forming galaxies with a thin stellar disk quench and evolve into passively evolving galaxies without a violent morphological change as discussed in the previous section, it is understood that the fraction of passively evolving galaxies with a thin shape increases with time at z  1 rather than higher redshifts. Since more massive star-forming galaxies have a sufficient time to form a thin disk through the hot-mode accretion in earlier epochs in this scenario (Noguchi 2019), passively evolving galaxies with a thin shape may also be provided preferentially in more massive galaxy populations at z ∼ 1. This could explain our result that passively evolving galaxies with Mstar = 1010.5–1011 M⊙ already show a thinner shape than those with 1010–1010.5 M⊙ at 0.6  z  1.0. In fact, Bezanson et al. (2018) reported that ∼64% of quiescent galaxies with Mstar ∼ 1010.5–1011 M⊙ at 0.6  z  1.0 show a significant rotation, while those massive galaxies with Mstar > 1011 M⊙ show no or little rotation in the LEGA-C survey. Such quiescent galaxies with a significant rotation might be recently quenched galaxies with a relatively thin disk that has grown through the hot-mode gas accretion since z ≲ 2.
Citation: Dekel et al. 2009b
Citation function: Compare/Contrast
Optical-NIR Spectral Energy Distribution (SED) modelling of diffuse light (e.g. Carson et al. 2015; Dale et al. 2016) at high spatial resolution can provide a map of the spatial variation and composition of the main stellar population components of the NSC, as well as their host galaxy. Such analysis can also unveil (an additional) contribution to the optical-NIR light from accretion on to an obscured nuclear MBH and/or nuclear star formation activity (e.g. Noll et al. 2009; Drouart et al. 2016). To understand NSC formation, it is therefore critical to be able to disentangle such degeneracies. For example, follow-up spectroscopic observations aiming at decomposing the main stellar populations and the dynamical imprint of a MBH rely on a good mass model to predict the stellar population velocity profile (e.g. Häring-Neumayer et al. 2006; Seth et al. 2010; Neumayer et al. 2011; Neumayer & Walcher 2012; Nguyen et al. 2018). Observations of NSCs have shown that although they contain young stellar populations and extended star formation histories (SFHs), the most dominant one by mass is the oldest (≳3 Gyr), where more than 50 per cent of the mass of the cluster has formed (e.g. Walcher et al. 2006; Kacharov et al. 2018, from spectral modelling in the optical). Therefore, characterizing the spatial structure of NSCs in the NIR is of particular importance, because that is where most of the stellar light of the old stellar population is emitted that allows us to trace most of the gravitating mass. Therefore, characterizing NSCs in the NIR can provide additional constraints as to which of the leading NSC formation scenarios had a major role in the formation of particular NSC. However, to be able to achieve this for a larger sample and of more distant galaxies, efficient high spatial resolution observations are required, such as with the presented here wide-field ground-layer AO NIR observations in the NIR with ARGOS at the LBT.
Citation: Häring-Neumayer et al. 2006
Citation function: Motivation
The initially used automated methods for detection of solar activity in the solar images are based on the predefined rules inferred from the appearance and usual characteristics of the structures in the solar atmosphere (Henney & Harvey 2005; Krista & Gallagher 2009; Pérez-Suárez et al. 2011). However, it is not possible to capture all the unique solar structures using generally defined rules. Therefore, methods with more advanced mathematical algorithms were introduced. The Spatial Possibilistic Clustering Algorithm (SPoCA) (Barra et al. 2009; Verbeeck et al. 2014) or Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) (Garton, Gallagher & Murray 2018) have been found to be very effective and are widely used in online solar data visualization tools1$^,$ .2 The SPoCA also provides entries for catalogues of coronal holes and active regions within the Heliophysics Events Knowledgebase (HEK) (Hurlburt et al. 2012) that is commonly used in the SolarSoft (Freeland & Handy 1998) and SunPy (The SunPy Community et al. 2020) frameworks. As will be presented later, these algorithms still have limitations for the precise segmentation of structures in the solar corona. Due to the advances in computer vision in recent years, approaches based on machine-learning techniques are able to extend the standard methods (Aschwanden 2010). Conventional machine-learning techniques as support vector machine (SVM), Decision Tree, or Random Forest could improve the detection of coronal holes as they provide automated distinguishing from filaments in EUV solar images (Reiss et al. 2015; Delouille et al. 2018). On the assumption that the techniques based on convolutional neural networks (CNN) are the most convenient techniques for image segmentation tasks (Lecun, Bengio & Hinton 2015), a new era in automated processing of solar images has begun. Illarionov & Tlatov (2018) demonstrated that for the segmentation of coronal holes, CNN provides quantitatively comparable results as the SPoCA and CHIMERA algorithms. Jarolim et al. (2021) provided a method called CHRONOS based on CNN with progressively growing network approach for robust segmentation of coronal holes.
Citation: Garton, Gallagher & Murray 2018
Citation function: Background; Background; Background
More recently, Lyman α haloes (LAHs) have been discovered around star-forming galaxies that show Ly α emission far beyond the galaxies’ optical bodies, tracing the circumgalactic rather than interstellar gas (e.g. Hayes et al. 2013). While LAHs are fainter and smaller than LABs in their Ly α extent, they might be a generic feature around Ly α emitting galaxies. Narrow-band imaging can efficiently detect LAHs at targeted redshifts through stacking (Hayashino et al. 2004; Steidel et al. 2011; Matsuda et al. 2012; Feldmeier et al. 2013), and narrow-band surveys enable ultradeep, blind samples of LAHs around distant galaxies (Momose et al. 2014, 2016; Kakuma et al. 2019). In the last years modern surveys performed with integral field unit (IFU) spectrographs on 10 m-class telescopes, such as the Multi Unit Spectroscopic Explorer (MUSE) and the Keck Cosmic Web Imager (KCWI), take place. These new instruments allow the study of individual, faint LAHs opposed to previous narrow-band stacks. Along with the IFUs’ spectral resolution these recent surveys largely increase the information available from LAH observations. Hundreds of individually extended Lyman α haloes at z ≳ 2 have been revealed since (Wisotzki et al. 2016). Many of these are specifically targeted samples which focus on bright quasars (Borisova et al. 2016; Cai et al. 2019; Guo et al. 2020; O’Sullivan et al. 2020), based on strong earlier evidence of enhanced Ly α emission around active galactic nuclei (AGNs Cantalupo et al. 2014; Arrigoni Battaia et al. 2016; Arrigoni Battaia et al. 2019; Farina et al. 2019). Others exploit the large field of view of MUSE, in particular, to conduct blind surveys for LAHs around more typical, generally star-forming galaxies (Leclercq et al. 2017; Wisotzki et al. 2018; Leclercq et al. 2020). At the same time, follow-up with other instruments such as ALMA reveals complementary views on other gas phases within LAHs including CO (Emonts et al. 2019).
Citation: Momose et al. 2014
Citation function: Background; Background