adsabs/FOCAL huggingface dataset:
The standard photometric analysis (see Dalessandro et al. 2008a, 2008b) has been performed on the “flc” images, which are corrected for flat field, bias, dark counts, and charge transfer efficiency. These images have been further corrected for “Pixel-Area-Map”10

10
For more details see the ACS Data Handbook.
 with standard IRAF procedures. By using the DAOPHOT II ALLSTAR and ALLFRAME packages (Stetson 1987), we performed an accurate photometric analysis of each image. First of all, we modeled the point-spread function (PSF) by using a sample of ∼200 bright but not saturated stars. The model has been chosen on the basis of a χ2 test and, in every image, the best fit is provided by a Moffat function (Moffat 1969). Then we performed a source detection analysis, setting a 3σ detection limit, where σ is the standard deviation of the measured background. Once a list of stars was obtained, we performed a PSF-fitting in each image. In the resulting catalog we included only stars present at least in half the images for each filter. For each star, we homogenized the magnitudes estimated in different images, and their weighted mean and standard deviation have been finally adopted as the star mean magnitude and its related photometric error (see Ferraro et al. 1991, 1992). However, in order to perform variability studies, for each source we also kept the homogenized magnitude measured in each frame in both filters. Then, instrumental magnitudes have been calibrated to the VEGAMAG system, cross-correlating11

11
We used CataXcorr, a code that is specifically developed to perform accurate astrometric solutions. It has been developed by P. Montegriffo at INAF–Osservatorio Astronomico di Bologna. This package is available at http://davide2.bo.astro.it/paolo/Main/CataPack.html, and has been successfully used in a large number of papers by our group in the past ten years.
 our catalog with that by Anderson et al. (2008), using the ∼7600 stars in common.
Citation: Moffat 1969
Citation function: Uses
Understanding how, when, and where galaxies quench their star formation is one of the most muddled, outstanding problems in galaxy formation. Different models for galaxy formation and evolution often make very different assumptions and/or predictions. Among them, semi-analytic galaxy formation models (SAMs; e.g., White & Frenk 1991; Kang et al. 2005; Bower et al. 2006; Croton et al. 2006; Bower et al. 2008; Somerville et al. 2008; Parry et al. 2009; Guo et al. 2011; Lu et al. 2011, 2014; Gonzalez-Perez et al. 2014; Lee et al. 2014; Henriques et al. 2015; Ruiz et al. 2015; Somerville et al. 2015) and hydrodynamical simulations (e.g., Katz et al. 1992; Springel et al. 2001, 2005; Springel & Hernquist 2003; Kereš et al. 2009; Angulo et al. 2012; Vogelsberger et al. 2014a, 2014b; Schaye et al. 2015) are two powerful tools to trace galaxy formation and evolution in cosmological volumes. SAMs are phenomenological models that use an approximate/empirical formula to describe all baryonic processes relevant to galaxy formation, such as gas accretion, heating and cooling, star formation, feedback from stars and AGN, mergers, and stripping due to tides and ram pressure. Because of their different locations within host halos, centrals and satellites in SAMs are assumed to undergo different quenching processes (see, e.g., Henriques et al. 2017). For example, the stripping of hot and cold gas associated with galaxies is assumed to only act on satellites. In addition, the efficiency of radio AGN feedback is assumed to depend on the associated hot gas mass, which may be very different between centrals and satellites. Hydrodynamical simulations, on the other hand, evolve the dark matter and baryonic components in a self-consistent way, though some assumptions have also to be adopted to model subgrid physics (see, e.g., Springel & Hernquist 2003). More importantly, centrals and satellites are not treated differently a priori, and their differences, if any, result directly from a complicated interaction between the baryonic content of a galaxy and its environment, while the (subgrid) modeling of star formation and feedback processes carry no knowledge of this environment.
Citation: Lee et al. 2014
Citation function: Background; Background
The lack of detection of supersymmetric particles at Large Hadron Collider led alternative candidates for dark matter to rapidly gaining attention. Apart from other particles, modifications of the gravitational action also offer a new avenue to account for the dark matter. Among many theories, MOG has been successfully tested with galaxies and cluster of galaxies showing the capability to explain the rotation curves of spiral galaxies, the gravitational lensing, the Sunyaev–Zeldovich effect, and the X-ray emission in galaxy clusters without resorting to any dark matter component (Brownstein & Moffat 2006; Moffat & Toth 2013; Moffat & Rahvar 2013, 2014; Moffat 2016; Banerjee et al. 2017; De Martino & De Laurentis 2017; Lopez Armengol & Romero 2017). Nevertheless, a recent study of the dynamics of dwarf spheroidals orbiting around the Milky Way has shown some inconsistencies with previous results (Haghi & Amiri 2016). Since dwarfs are supposed to be dominated by dark matter, any theory that aims to replace it with a modification of the gravitational potential must also be able to account for the dynamics of these galaxies. Here, we have investigated the dynamics of stars in Antlia II in the framework of MOG theory. Antlia II is a recently discovered low-surface-brightness dwarf galaxy being ∼100 more diffuse than ultra diffuse galaxies. It has a very wide core ∼3 kpc, and it is supposed to be strongly dominated by dark matter (Broadhurst et al. 2019; Torrealba et al. 2019). Due to its own nature, it represents an ideal candidate to test alternative theories of gravity such as MOG. Therefore, we have predicted the dispersion velocity profile by solving the spherically symmetric Jeans equation, and assuming that the dynamics of stars is determined by the modified potential well in equation (2) arising in MOG weak field limit, and the stellar density profile is well described by the Plummer model in equation (5). Finally, we have projected the solution of Jeans equation along the line of sight to compare it with the data.
Citation: Haghi & Amiri 2016
Citation function: Differences
Observations are now probing galaxies in the middle of the reionization epoch, when the gas in the intergalactic medium was transformed from its initially neutral state into a hot, ionized plasma (e.g. McLure et al. 2011; Finkelstein et al. 2012; Bouwens et al. 2014). Most likely stars in galaxies are responsible for this transformation, although this heavily depends on the fraction of ionizing photons produced by the stars that make it into the intergalactic medium, the so-called escape fraction fesc. The escape fraction is a key parameter in studies of the contribution of the observed galaxy population to reionization (e.g. Bouwens et al. 2012; Robertson et al. 2013), semi-analytic modelling of reionization (e.g. Choudhury, Haehnelt & Regan 2009; Pritchard, Loeb & Wyithe 2010; Santos et al. 2010; Mesinger, Furlanetto & Cen 2011; Raskutti et al. 2012; Mitra, Ferrara & Choudhury 2013; Shull et al. 2012) and numerical simulations of reionization (e.g. Iliev et al. 2006; Trac & Cen 2007; Ciardi et al. 2012). A large effort is going into determining the escape fraction observationally. Except for two objects (Leitet et al. 2011, 2013), in the local Universe no ionizing radiation has been detected directly (Leitherer et al. 1995; Deharveng et al. 2001), although some objects show indirect evidence of photon leakage (Heckman et al. 2011; Zastrow et al. 2011). The lack of detections may be partly due to selection bias (Bergvall et al. 2013), but the objects from which radiation is detected have very low escape fractions, fesc  4 per cent. At z ∼ 1, no objects with leaking ionizing photons have been detected (Bridge et al. 2010; Siana et al. 2010), but at z ∼ 3, the highest redshift at which the opacity of the intergalactic medium for ionizing photons is approximately less than unity, ionizing photons have been detected in ∼10 per cent of the observed objects (Nestor et al. 2013). Attempts to constrain the escape fraction with numerical simulations find ranges between fesc  10 per cent (Razoumov & Sommer-Larsen 2006, 2007; Gnedin, Kravtsov & Chen 2008; Paardekooper et al. 2011; Kim et al. 2013) and fesc > 80 per cent (Wise & Cen 2009; Razoumov & Sommer-Larsen 2010), with likely a strong mass and redshift dependence (Yajima, Choi & Nagamine 2010; Wise et al. 2014). Due to the opacity of the intergalactic medium, we need to mostly rely on numerical simulations to learn about the escape fraction during the epoch of reionization.
Citation: Leitet et al. 2011
Citation function: Background; Background; Background
Citation: Mesinger, Furlanetto & Cen 2011
Citation function: Background; Background
Since the errors of the LIGO-estimated rates are dominated by Poisson statistics (Abbott et al. 2020a,b), we approximate the PDF for the expected number of detections $\mathcal {N}=\mathcal {R}VT$ (from the surveyed space–time volume VT) by $\mathrm{d}P/\mathrm{d}\mathcal {N}\propto \mathcal {N}^{k-1/2}\mathrm{e}^{-\mathcal {N}}/k!$, where k = 1 for each of the relevant cases ($\mathcal {R}_{190814}$, $\mathcal {R}_{170817}$, and $\mathcal {R}_{190425}$), and the factor of $\mathcal {N}^{-1/2}$ is from Jeffrey’s prior (Abbott et al. 2020a). From the median values of $\bar{\mathcal {R}}_{190814}=7\rm \, Gpc^{-3}\, yr^{-1}$ (Abbott et al. 2020a), $\bar{\mathcal {R}}_{\rm 170817}=760\rm \, Gpc^{-3}\, yr^{-1}$, and $\bar{\mathcal {R}}_{\rm 190425}=460\rm \, Gpc^{-3}\, yr^{-1}$ (Abbott et al. 2020b), we obtain the effective surveyed space–time volumes $VT=1.2/\bar{\mathcal {R}}$ for each of these three events (‘1.2’ is the median of $\mathrm{d}P/\mathrm{d}\mathcal {N}$). We consider both GW170817 and GW190425 as bNS mergers, because the component masses of GW190425 are not far from those of GW170817 and the nature of the merging objects makes little practical difference in our model. Thus, the PDF of the total bNS merger rate from the sum of the two is given by a convolution of the two individual PDFs
(1)$$\begin{eqnarray*}
{\mathrm{d}P\over \mathrm{d}\mathcal {R}_{\rm bns}} = \int _0^{\mathcal {R}_{\rm bns}} \mathrm{d}\mathcal {R}_1 {\mathrm{d}P\over \mathrm{d}\mathcal {R}_1} \left.{\mathrm{d}P\over \mathrm{d}\mathcal {R}_2}\right|_{\mathcal {R}_{\rm bns}-\mathcal {R}_1},
\end{eqnarray*}$$where we have written $\mathcal {R}_{1} = \mathcal {R}_{170817}$, $\mathcal {R}_{2} = \mathcal {R}_{190425}$ for brevity. We then calculate the PDF for the inverse of the total bNS merger rate $\mathrm{d}P/\mathrm{d}\mathcal {R}_{\rm bns}^{-1}=\mathcal {R}_{\rm bns}^2\mathrm{d}P/\mathrm{d}\mathcal {R}_{\rm bns}$. Finally, the PDF of the rate ratio $\beta =\mathcal {R}_{190814}/\mathcal {R}_{\rm bns}$ is given by
(2)$$\begin{eqnarray*}
{\mathrm{d}P\over \mathrm{d}\beta } = \int _0^\infty {\mathrm{d}\mathcal {R}_{3}\over \mathcal {R}_3} {\mathrm{d}P\over \mathrm{d}\mathcal {R}_{3}} \left.{\mathrm{d}P\over \mathrm{d}\mathcal {R}_{\rm bns}^{-1}}\right|_{\beta /\mathcal {R}_3},
\end{eqnarray*}$$where we have written $\mathcal {R}_{3} = \mathcal {R}_{190814}$ for brevity. We find the 90 per cent confidence interval for the rate ratio to be in the range $0.064\, \rm {per\, cent}\lt \beta \lt 2.8\, \rm {per\, cent}$.
Citation: Abbott et al. 2020b
Citation function: Uses; Uses; Uses
Citation: Abbott et al. 2020; b
Citation function: Uses; Uses
MSH 11-61A (also known as G290.1-0.8) is a mixed morphology SNR detected from radio to soft X-rays (up to ~3 keV) that was formed by the core collapse of a massive progenitor star (mass ≳25 M⊙;  Filipović et al. 2005; Reynoso et al. 2006; García et al. 2012; Kamitsukasa et al. 2015; Auchettl et al. 2015a). Following these authors, the distance to the SNR is in the range 6−11 kpc; the most recently determined values converge towards 7 ± 1 kpc. We adopt a distance of 7 kpc throughout the paper1. The INTEGRAL source IGR J11014-6103 is located close to MSH 11-61A and is powered by PSR J1101-6101 (Pavan et al. 2011, hereafter Paper I; Tomsick et al. 2012; Pavan et al. 2014, hereafter Paper II; Halpern et al. 2014). The pulsar shows spin-down parameters typical for pulsars of its age: a period of P = 62.8 ms and a pulse period derivative Ṗ = (8.56 ± 0.51) × 10-15 s s-1. The estimated spin-down energy is Ė = 1.36 × 1036 erg s-1 and the surface dipolar magnetic field is 7.4 × 1011 G (Halpern et al. 2014). Previous Chandra observations aimed at the INTEGRAL source showed that PSR J1101-6101 simultaneously powers several outflows: an X-ray and radio PWN, shaped in a narrow cone elongated towards the parent SNR, and an X-ray jet and counter-jet, both oriented nearly perpendicular to the PWN axis (Tomsick et al. 2012; Paper II). The main jet extends for nearly 5′ in the sky, which corresponds to a projected length of ~11 pc, and showed a remarkable helicoidal pattern (see Paper II). Already in the data set analysed in Paper II, indications for a spatial deviation from the helical pattern of the main jet were noticed at a distance of ~50″ from the pulsar. At this position the surface brightness of the jet was low, forming what looked like a gap, but its brightness profile was compatible with expectations of Doppler-deboosting in the jet-helix model. The spatial deviation was therefore not considered significant at the time because the data were hampered by the presence of CCD chip gaps, resulting in only 50% effective exposure in that region. The counter-jet was detected at 3.7σ in the Chandra image and its flux was estimated to be ~5% that of the main jet. The conical shape of the PWN in IGR J11014-6103 was ascribed to the supersonic motion of PSR J1101-6101 in the ISM (Tomsick et al. 2012).
Citation: Halpern et al. 2014
Citation function: Background
In a collisional thick-target model, the HXRs are produced by collisional bremsstrahlung during the passage of non-thermal electrons through denser plasma regions, in which the electrons are stopped completely by Coulomb collisions (Brown 1971; Brown et al. 2009; Kontar et al. 2011). Using the electron distribution parameters derived from RHESSI spectroscopy, the power delivered by non-thermal electrons above low-energy cutoff (ELC) can be calculated by the expression
2





where ELC is the low-energy cutoff, Fe is the total number of electrons per second above ELC in units of 1035 electrons s−1, and δ is the electron spectral index (Fletcher et al. 2013). An accurate determination of the low-energy cutoff to non-thermal electron distributions is crucial for the calculation of power and consequently non-thermal energy in solar flares (Sui et al. 2005; Veronig et al. 2005). In general flares are thought to have low-energy cutoffs close to or in the region where the emission is dominated by thermal bremsstrahlung (Ireland et al. 2013). We further emphasize that in flares with multiple HXR sub-peaks during the impulsive phase (like the present one), the determination of ELC is rather illusive during the peak emission as it is difficult to distinguish the signals of flare accelerated electrons against dominant thermal bremsstrahlung. This often results in the overestimation of ELC during HXR peak phases. For this event, we find that ELC varies in the range of 20–32 keV with relatively higher values (∼32 keV) during the second peak (see Figure 13(d)). Therefore, in order to get an estimation of power (and consequently energy) of the non-thermal electrons, we have taken ELC as 25 keV, which is the average of ELC over the flare time interval. The estimated non-thermal electron flux (Fe) above 25 keV is plotted in Figure 14(b). In Figure 14(c), we have plotted the power (Pnth) and energy (Enth) contained in non-thermal electron beams. The plots of Fe and Pnth clearly indicate noticeable enhancement in the particle rate and consequently the power of flare-accelerated electrons during the two HXR peaks.
Citation: Sui et al. 2005
Citation function: Motivation
During the past decade, significant information has been gathered on the chemical compositions of post-asymptotic giant branch (AGB) stars in the Milky Way. This has led to the discovery of a class of post-AGB stars that have C/O > 1 and display extreme enrichment in the abundances of the elements heavier than Fe produced by slow neutron captures (the s-process, Van Winckel & Reyniers 2000; Reyniers & Van Winckel 2003; Reyniers et al. 2004). Since AGB stars can become C rich and have been confirmed both theoretically and observationally as the main stellar site for the s-process (see, e.g., Busso et al. 2001), it is natural to interpret these post-AGB observations as the signature of the nucleosynthesis and mixing events that occurred during the preceeding AGB phase. These events are currently identified as (i) the mixing of protons into the radiative He-rich intershell leading to the formation of a thin region rich in the main neutron source 13C (the 13C “pocket”); (ii) proton-ingestion episodes (PIEs) inside the convective thermal pulses (TPs); and (iii) the third dredge-up (TDU), which carries C and s-process elements from the He-rich intershell to the convective envelope and to the stellar surface. Since the details of all these processes are very uncertain (see discussion in, e.g., Busso et al. 1999; Herwig 2005; Campbell & Lattanzio 2008), observations of post-AGB stars provide strong constraints. Recent observations of the chemical composition of four low-metallicity ([Fe/H] from −1.15 to −1.34), s-process-rich, C-rich post-AGB stars in the Large (J050632, J052043, and J053250) and Small (J004441) Magellanic Clouds (LMC and SMC, respectively) have provided a challenge to AGB s-process models (De Smedt et al. 2012, 2014; van Aarle et al. 2013)1. Since we know the distance of these stars, it is possible to determine from the observed luminosity that their initial stellar mass was in the range 1–1.5 M⊙. Stellar AGB models in this range of mass and metallicity can produce the high observed abundances of the s-process elements, such as Zr and La (1  [Zr/Fe]  2 and 1  [La/Fe]  3), together with [Pb/La] ≃ 1, if a deep TDU is assumed after a last TP. Instead, negative [Pb/La] values are observed as upper limits (De Smedt et al. 2014). Here we test different possible modifications of the current AGB s-process scenario to explain the neutron-capture abundance pattern observed in the MC post-AGB stars.
Citation: Busso et al. 2001
Citation function: Background; Background
Figure 11 shows that the distribution of the spectral index α (see Table A.1) is bimodal suggesting the existence of two populations (disc-bearing and discless stars) with approximately the same number of sources. We verified that the bimodality of the distribution of spectral indices is not an artefact caused by the different number of points and wavelength range of the photometric data available for each star to compute the spectral indices. A similar result was also observed for the Chamaeleon I star-forming region (see Fig. 11 of Luhman et al. 2008). Previous studies suggested that the early disappearence of circumstellar discs could be related to environmental effects imposed by the presence of massive stars that produce strong UV radiation, stellar winds, and supernova explosions (see e.g. Walter et al. 1994; Martin et al. 1998). However, we see no dependency of the spectral index on the position of the stars in our sample and any nearby OB star surrounding the Lupus clouds to support this scenario. As shown in Fig. 10 we note the existence of only a few stars older than 10 Myr in our sample which could be potential contaminants (as expected based on the performance of our classifier, see Table 1). Thus, the hypothesis of contamination by older field stars does not explain the bimodality of spectral indices in our sample given that the two populations have the same number of stars, similar ages and are younger than the potential contaminants from the Sco-Cen association. Alternatively, we investigated the dependency of the spectral indices on the age and colour of the stars. Figure 12 shows that the age distribution of the Class II and Class III stars in Lupus overlap. The median age of Class III stars inferred from the Baraffe et al. (2015) and Siess et al. (2000) stellar models is about 3 Myr which yields a rough estimate of the typical disc lifetime in the Lupus association. However, it should also be noted from Fig. 12 that we observe an excess of Class III stars at cooler temperatures (red colours) suggesting that the survival time of circumstellar discs may also depend on other stellar parameters. For example, Galli et al. (2015) used an empirical disc evolution model to determine the lifetime of circumstellar discs in Lupus in terms of the mass of the star. According to their model the average lifetime of a circumstellar disc around a star with 0.1 M⊙ is of the order of 1 Myr which could explain the early disapperance of circumstellar discs for some stars in our sample.
Citation: Martin et al. 1998
Citation function: Background; Differences
The top-down dynamical approach consists in producing ab initio simulations of Milky Way-like galaxies in a cosmological context. This approach can be useful to understand some general features of galaxy formation (e.g. Minchev et al. 2014). However, it is not flexible enough to produce an acceptable model for the wide range of extremely detailed data soon to be available for our own Galaxy. On the other side, the bottom-up approach for dynamical modelling consists in starting from the actual Galactic data, rather than from simulations, in order to construct a model of the Galaxy. To avoid the redundancy and computational waste of representing the orbits of every single particle in the model, one can use a phase-space distribution function (DF) to represent each population of constituent particles (typically, various stellar populations and dark matter; see e.g. Binney & Piffl 2015; Piffl, Penoyre & Binney 2015). The model-building generally starts from the assumptions of dynamical equilibrium and axisymmetry. These assumptions allow us to make use of Jeans’ theorem constraining the DF to depend only on three integrals of motion, which can typically be chosen to be the radial, azimuthal, and vertical action variables. However, one should remember, especially when modelling the stellar populations of the Galactic disc, that the Galaxy is obviously not axisymmetric, as it harbours a central bar as well as spiral arms. Such perturbations can obviously be treated through perturbation theory, whose foundations in the case of flat 2D discs have been laid down by Kalnajs (1971). For instance, following up on the work of Binney & Lacey (1988) who derived the orbit-averaged Fokker–Planck equation for a 2D stellar disc, recent investigations (e.g. Fouvry, Binney & Pichon 2015) have focused on the long-term secular evolution of such a flat disc by means of diffusion through action space at resonances, producing ridges in action space. Here, we are rather interested in the present-day perturbed DF in the action-angle space of the unperturbed Hamiltonian, in the presence of a 3D spiral arm perturber, which could be fitted to a snapshot of the Galaxy taken by current and upcoming large surveys. Our philosophy is thus closer to that of McMillan (2013), except that the shape of the perturbed DF will be computed directly from the linearized Boltzmann equation. Moreover, in this paper, we will first concentrate only on the response away from the main resonances, the extremely interesting effects expected at resonances, as well as the effect of resonance overlaps of multiple perturbers (e.g. Quillen 2003; Minchev & Famaey 2010), being the subject of further analytical work.
Citation: Binney & Piffl 2015
Citation function: Uses