adsabs/FOCAL huggingface dataset:
We mentioned that our light curves are characterized by two large gaps of one year and seven months plus eight months, as shown in Table 1. From the table, it is apparent that mean and median observed baseline values, computed for the sources in the main sample for each season, are very close to the maximum observed baseline (when not exactly coincident with it) for the corresponding season. Similarly, the mean and median number of visits for each season are very close to the total number of visits (when not exactly coincident with it) for the corresponding season. This shows how, for individual seasons, our dataset can take advantage of a dense sampling, which plays a key role in the context of AGN detection efficiency, as shown in De Cicco et al. (2019). Nevertheless, the two gaps affect the shape of our SF with inadequate sampling in correspondence with some timescales. Sparse and/or irregular sampling is a very common issue in SF analysis (e.g., de Vries et al. 2003; Peters et al. 2015; Simm et al. 2016; Sartori et al. 2019). Indeed, there are works from the literature where the cadence is low but the sampling is regular: as an example, Hawkins (2002) uses quasar light curves from a long-term monitoring program with 24 yearly observations per source to investigate the origin of the emission mechanism in AGN. Nonetheless, one of the advantages in the use of the SF is its relative insensitivity to irregular sampling when sources are considered as an ensemble rather than individually (e.g., Hawkins 2007; Kozłowski 2016; Sartori et al. 2019). As mentioned in Sect. 3.2, Bauer et al. (2009) analyze the effect of irregular sampling by means of simulations, and conclude that the turnover that is observed in the light curve is an effect of sparse sampling at longer timescales, and not a real feature in the SF of AGN. Emmanoulopoulos et al. (2010) also resort to simulations in order to assess whether and to what extent the SF is robust against the presence of gaps in the light curves. They simulated a single light curve and then investigated the effect of three different gaps in the data, representing three different situations: almost periodic data gaps, dense and sparse sampling, and purely sparsely sampled data, corresponding to 57%, 83%, and 92% of the data being removed from a single simulated light curve that is 2000 time units long, respectively. They then used bootstrapping to extract 1000 light curves from each of the obtained light curves with gaps, then compared the results obtained with and without gaps. While they find that the presence of gaps is responsible for the presence of wiggles and bends in the SF, from the right panels of their Fig. 12 we can infer that these wiggles and bends do not alter the slope of the SF obtained from the light curve with no gaps. In this work we are not investigating the turnover as our baseline is not long enough (Sect. 3.2); our analysis is instead focused on the linear region of the SF (and the possible dependence on physical quantities of interest), where the irregularity of the sampling does not constitute a major issue.
Citation: Peters et al. 2015
Citation function: Motivation
With a priori knowledge of speckle evolution lifetime (Milli et al. 2016), more evolved a posteriori algorithms may well calibrate the speckle pattern. However, any such method can directly benefit from an active technique that minimizes the static or quasi-static speckles in each science image during an observation. Active suppression of these speckles requires measurement of the electric field associated with the speckles directly from a coronagraphic image using a focal plane wavefront sensor (FPWFS). Several FPWFSs have been proposed such as phase diversity (Bordé & Traub 2006; Give’on et al. 2007; Sauvage et al. 2012) and the self-coherent camera (SCC; Baudoz et al. 2006, 2012). Once the electric field is measured, one or several deformable mirrors (DM) can then be used to minimize the speckle intensity in a region of the image called the dark hole (Malbet et al. 1995). Very encouraging laboratory results have been obtained on the coronagraphic testbeds simulating the space-related environment. The stellar speckle intensity is shown to be reduced by a factor of up to 105 (Belikov et al. 2007; Trauger et al. 2011, 2012; Mazoyer et al. 2013, 2014) which would enable the detection of planets 1010 times fainter than their host star. Several attempts on ground-based Extreme-AO instruments have been performed with moderate results. The stellar speckle intensity has been suppressed by only a factor of up to ten (Savransky et al. 2012; Martinache et al. 2014; Bottom et al. 2017; Matthews et al. 2017; Wilby et al. 2017; Vigan et al. 2019; Galicher et al. 2019) reaching contrast levels of roughly 10−6. Most of these techniques temporally modulate the speckle intensity to measure their phase and at least three images are needed for each estimation of the electric field. The quasi-static speckles that evolve faster than every four images cannot be correctly estimated and therefore set a limitation for the speckle estimation. Addressing this concern, our team proposed the SCC that spatially modulates the speckle intensity so that the associated complex electric field can be measured in every science image. The drawback is that a finer sampling of the coronagraphic image is required as compared to the temporal modulation techniques. The SCC has been developed and rigorously tested in space-related environments on the THD2 bench at the Paris Observatory (Baudoz et al. 2018).
Citation: Bordé & Traub 2006
Citation function: Background
Ultraluminous X-ray sources (ULXs) have been widely observed in the local Universe, with inferred isotropic luminosities above 1039 erg s−1 (Roberts 2007; Feng & Soria 2011). Those below ∼3 × 1039 erg s−1 can be readily associated with accretion on to stellar mass black holes (BHs) (∼10 M⊙) accreting close to or at their Eddington limit (see Sutton, Roberts & Middleton 2013, and references therein). There is now strong evidence to support this assertion, with the discovery of extremely bright ballistic jets from a ULX in M31 (Middleton et al. 2013; Middleton, Miller-Jones & Fender 2014b), which unambiguously links the flow with Eddington rate accretion (Fender, Belloni & Gallo 2004), and the first dynamical mass measurement of the compact object in a ULX, from M101 ULX-1 (Liu et al. 2013). Observations of such ‘low-luminosity’ ULXs (Middleton et al. 2011b, 2012; Kaur et al. 2012; Soria et al. 2012) have revealed changes in the disc emission that may imply the creation of a radiation pressure supported, larger scaleheight flow in the inner regions (Middleton et al. 2012) or magnetic pressure support (Straub, Done & Middleton 2013). Although emission below ∼2 keV is generally heavily photoelectrically absorbed by material in the Galactic plane (e.g. Zimmermann et al. 2001), similar spectral behaviour may also be seen in a small number of Galactic BH X-ray binaries (BHBs) at high rates of accretion (e.g. Ueda, Yamaoka & Remillard 2009; Uttley & Klein-Wolt, in preparation). Such ‘extreme’ high state BHBs probably dominate the ULX population (Walton et al. 2011), yet a significant number of ULXs can still be found at higher luminosities. Those above 1041 erg s−1 are dubbed hyperluminous X-ray sources (HLXs; Gao et al. 2003) and provide the best evidence (Farrell et al. 2009; Davis et al. 2011; Servillat et al. 2011; Webb et al. 2012) for a population of intermediate-mass BHs (IMBHs; Colbert & Mushotzky 1999). Such IMBHs (with masses above those expected from direct stellar collapse: >100s of M⊙) could potentially be formed in globular clusters (Miller & Hamilton 2002, but see Maccarone et al. 2007), through capturing and tidally stripping a dwarf galaxy (King & Dehnen 2005) or mergers in young super star clusters (Portegies-Zwart, McMillan & Gerhard 2003; Portegies-Zwart et al. 2004).
Citation: Middleton et al. 2011b
Citation function: Background; Background; Background
Models of the ECSN progenitor cores suggest the onset of the electron-capture instability occurs at a unique ONeMg core mass in the mass range of 1.366–1.377 M⊙. (Miyaji et al. 1980; Nomoto 1984, 1987; Podsiadlowski et al. 2005; Takahashi et al. 2013). Electron captures cause the core to contract, and O and Ne burning is ignited in the central regions and propagates outwards in a deflagration front (Schwab et al. 2015), processing material to nuclear statistical equilibrium, where further electron captures and photdissociation accelerates the collapse (Miyaji et al. 1980; Nomoto 1987; Takahashi et al. 2013). Whether the core collapses or the deflagration disrupts the core depends sensitively on the ignition density (Isern et al. 1991; Jones et al. 2016). If the core does collapse, the explosion proceeds via delayed explosion on short timescales (Mayle & Wilson 1988; Kitaura et al. 2006; Fischer et al. 2010), and 2D simulations suggest the explosion occurs before significant convection has had time to develop (Wanajo et al. 2011) and hence a symmetric explosion results. This, coupled with the steep density gradient at the core surface, leads to very little mass loss from the core; estimates of mass loss include of order 10−3 M⊙ (Podsiadlowski et al. 2005), 10−2 M⊙ (Kitaura et al. 2006), and 1.39 × 10−2 M⊙ (1.14 × 10−2 M⊙) for the 1D (2D) models of Wanajo et al. (2009, 2011). Therefore the ONeMg progenitor core mass is a good estimate of the baryon mass MB of the resulting NS (Podsiadlowski et al. 2005). Indeed, PSR J0737-3039A and the companion to PSR J1756-2251 have gravitational masses consistent with baryon masses ∼1.37 M⊙ when their gravitational binding energies are taken into account (Lattimer & Yahil 1989). Population synthesis calculations incorporating the various binary evolution channels that might lead to production of NSs via ECSNe show that J0737-3039B most likely formed in an ECSN, and the companion to PSR J1756-2251 is consistent with such a formation scenario (Andrews et al. 2015). Other systems with candidates for ECSNe formation also exist (Keith et al. 2009; Chen et al. 2011).
Citation: Fischer et al. 2010
Citation function: Compare/Contrast; Compare/Contrast; Compare/Contrast
Citation: Isern et al. 1991
Citation function: Uses
An empirical expression for FUV extinction based on UV reddening of a diverse, UV-selected sample of 200 galaxies (Seibert et al. 2005) is
(10)
\begin{eqnarray*}
A_{\rm FUV}(\beta)=3.978(m_{\rm FUV}-m_{\rm NUV})+0.143,
\end{eqnarray*}
where mFUV and mNUV are the respective GALEX AB magnitudes.13 The equation is similar to relations derived by others (e.g. Hao et al. 2011). Fig. 9 shows the SFRS galaxies in the IRX–(mFUV − mNUV) (or equivalently IRX–β) space. There is a correlation between AFUV(β) and AFUV(IRX) with Pearson correlation coefficient r = 0.71 and mean 〈AFUV(β) − AFUV(IRX)〉 = 0.33 mag, but the rms scatter in AFUV(IRX) as derived from AFUV(β) is 0.44 dex. Galaxies with $A_{\rm FUV}(\beta)\lesssim 2$ can have bolometric extinctions as high as 6 mag, and AFUV(β) applied to LFUV greatly underestimates their FIR luminosity and therefore SFR. This is consistent with other results (e.g. Kong et al. 2004; Johnson et al. 2006, 2007), which have shown that galaxies having higher current SFR relative to their past averaged SFR are likely to deviate above the IRX–β relation, i.e. have larger AFUV(IRX) for a given AFUV(β). Despite this qualitative agreement, the Kong et al. mean numerical relation for their UV-selected sample of 50 local starbursts is not a good fit to the FIR-selected SFRS data as shown in Fig. 9. Regardless of numerical values, all these studies agree that galaxies with higher SFR are more obscured at fixed β (also see Iglesias-Páramo et al. 2004; Cortese et al. 2006; Moore et al. 2010). At the low SFR end, galaxies with L60  109.3 L⊙, which at $z$ ≈ 0 are mostly early-type cluster galaxies, form two groups. Around 75 per cent of them are near the mean IRX–β relation, but the rest show AFUV(IRX) ≪ AFUV(β). One possibility is that these galaxies have older stellar populations with intrinsically high values of β. In the middle range 109.3 L60  1010.7 L⊙, there is a general trend for AFUV(IRX) to follow AFUV(β) but with rms scatter ∼0.34 dex. At L60 > 1010.7 L⊙, the scatter is ∼0.56 dex.
Citation: Kong et al. 2004
Citation function: Similarities; Similarities; Differences
Finally, the final estimates depend slightly on whether very strong lines with X > −6 are used or not. In Figure 7, very strong lines clearly show a systematic tilt. These strong lines have an impact on the slopes, e.g., seen in Figure 4. The lower 





 values of the stronger lines in MB99 would give higher 





 values with a fixed ξ, but this would also cause a tilt in Figure 4. A larger ξ is therefore required so that 





 values of strong and weak lines get balanced. While this is an important difference between the two line lists, generally speaking, it is suggested that using very strong lines often introduces complications such as non-LTE effects into a chemical abundance analysis (e.g., Kovtyukh & Andrievsky 1999; Gratton et al. 2006; Takeda et al. 2013). Based on synthetic spectra, we found that, in the case of lines with X ≳ −6, the line core does not grow any more with increasing metallicity and the damping wing starts to contribute to the EW at around the solar metallicity. If we run the bootstrap method with the same lines but including those with X > −6, we obtain moderately different results for the MB99 list, as illustrated in Figure 5. Four lines from MB99 have X > −6, and including them leads to higher ξ and lower 





 values: 





 = (1.47 ± 0.18, 6.94 ∓ 0.05) for Arcturus and (1.61 ± 0.16, 7.71 ∓ 0.06) for μ Leo. The changes caused by including the strongest lines are marginally significant, 1–2σ, for the former but are negligible for the latter. Figure 6 shows that one line, Fe i λ 11973.04, with the largest 





 has a particularly strong impact on the slope in the X versus 





 diagram for Arcturus with MB99. The same line gives 





 ∼ 8.10 dex, which is also higher than the average, for μ Leo. However, the scatter of 





 from lines within the low-X range is large, which explains the relatively small effect of including the high-X lines for μ Leo. In contrast, six VALD3 lines that we selected have X > −6, but including them has a negligible impact on the 





 measurements. For VALD3, the Fe i λ 11973.046 line leads to 





 values that are very close to the average abundances from other lines for both Arcturus and μ Leo. This line corresponds to the rightmost point in Figure 7 and has a very large difference, 0.8 dex, between the 





 values in the two line lists. Considering these complications, we decided to adopt the 





 values obtained without the lines at X > −6 as our best estimates. Although the 





 from individual lines depend on ξ as described above, we found that the [Fe/H] obtained in different works are not correlated with ξ (Figure 8). This is probably because systematic differences in previous works, such as differences in line lists and atmosphere models, introduced a scatter larger than the expected correlation between the two parameters.
Citation: Kovtyukh & Andrievsky 1999
Citation function: Uses
The advent of the ESA Gaia satellite for the first time allows the study of young stellar populations on large scales (> 100 pc) in six-dimensional (6D) position and velocity space. For example, using Gaia Data Release 2 (DR2) (Gaia Collaboration 2018a), Kounkel et al. (2018) and Zari et al. (2019) demonstrated that the Orion star-forming region is composed of a variety of populations with different spatial and kinematic properties each that are all likely generated in multiple events instead of in a progressive star formation history (see also Beccari et al. 2017; Jerabkova et al. 2019b; Kroupa et al. 2018, discussing three bursts of star formation in the Orion Nebula cluster). Zari et al. (2018) described the 6D properties and age structures in which young stars are found within a region of 500 pc around the Sun, and a number of other studies focusing on individual star-forming regions have been reported. One example is the large-scale picture of the Gamma Velorum region (Beccari et al. 2018; Cantat-Gaudin et al. 2019). The Gaia DR2 catalogue also led to the discovery of stellar relic filaments. These are spatial structures of a few pc that are more than 90 pc long and consist of stars of equal ages that are younger than a few hundred million years (Jerabkova et al. 2019a; Beccari et al. 2020). A Galaxy-wide survey using the Gaia DR2 catalogue reveals many elongated structures made of coeval stars, many of which are tidal tails of dissolving star clusters (Kounkel & Covey 2019). Meingast et al. (2021) described the nearby extended spatial distribution of stars that co-move with their clusters but are not bound to them. After the release of the Gaia DR2 catalogue, tidal tails have been found around four nearby ( 300 pc) OCs, namely Blanco 1 (≈100  Myr, Zhang et al. 2020), the Hyades (≈600 − 700 Myr; Meingast & Alves 2019; Röser et al. 2019; Douglas et al. 2019; Gossage et al. 2018; Reino et al. 2018; Lodieu 2020; Gaia Collaboration 2018b), Coma Berenices (≈750 Myr; Tang et al. 2019; Fürnkranz et al. 2019), and Praesepe (≈800 Myr; Röser & Schilbach 2019).
Citation: Beccari et al. 2017
Citation function: Background; Background; Background
In this section, we describe our new compilation of SLS. To construct Dobs, we have chosen only systems with spectroscopically data well measured from different surveys. We have considered 19 SLS from the CASTLES, 107 from SLACS, 38 from BELLS, 4 from LSD, 35 from SL2S, and 1 system from the DES survey. The final list has a total of 204 systems, being the largest sample of SLS to date. We use spectroscopy to select those lenses with lenticular (S0) or elliptical (E) morphologies that have been modelled assuming a SIS ($\sim 3{{\ \rm per\ cent}}$) or SIE ($\sim 97{{\ \rm per\ cent}}$) lens model. Many systems have not been taken into account due to several issues. For instance, the system PG1115+080 (Tonry 1998) from the CASTLES survey has been discarded because the lens mass model is steeper than isothermal. In addition, the system MGJ0751+2716 (Spingola et al. 2018) was also discarded because the main lens belongs to a group of galaxies. From the SLACS survey (Bolton et al. 2008; Auger et al. 2009), we remove the systems SDSSJ1251−0208, SDSSJ1432+6317, SDSSJ1032+5322, and SDSSJ0955+0101 since the lens galaxies are late-type. The same reason is applied to the systems SDSSJ1611+1705 and SDSSJ1637+1439 from the BELLS survey (Shu et al. 2016). We have also discarded the systems SDSSJ2347−0005 and SDSSJ0935−0003 from the SLACS survey and the system SDSSJ111040.42+364924.4 from the BELLS survey because they have large measured velocity dispersions (∼400 km s−1 or bigger values), suggesting the lens might be part of a group of galaxies or that there is substructure in the line of sight. For those systems without reported velocity dispersion error, we assumed the average error of the measurements in the survey subsample as follows. For the nine systems from CASTLES, we consider the average error on σ for this survey, i.e. 14  per cent. In the case of the system DES J2146−0047 (Agnello et al. 2015), we have assumed a 10 per cent error on σ, which is the average error of the entire sample. The LSD survey (Koopmans & Treu 2003; Treu & Koopmans 2004) reports σ corrected by circular aperture using the expression obtained by Jorgensen, Franx & Kjaergaard (1995a,b). A close inspection of the σspec values, with and without aperture correction, presented by Cao et al. (2015) show the differences is smaller than the reported error. Thus, we decided to use the observed values (σ) and the reported error for the sample without the aperture correction. On the other hand, in those systems in which the Einstein radius error was not reported, we followed Cao et al. (2015) and assumed an error of δθE = 0.05, which is the average value of the systems with reported errors in this sample.
Citation: Tonry 1998
Citation function: Compare/Contrast; Compare/Contrast
The shape of the spectral energy distribution and the emission rates of ionizing photons (see Table 1 of Paper II) depend on the assumed wind mass-loss rates, wind speeds, and wind clumping. These parameters are uncertain. Theoretical predictions are now available (e.g., Krtička et al. 2016; Vink 2017), but they have not yet been thoroughly tested against observations, because only very few stripped stars with sufficiently strong wind mass-loss have been identified and studied in detail so far (e.g., Groh et al. 2008). In Paper I, we showed that variations in wind mass-loss rate primarily affect the predicted emission rate of He II-ionizing photons, while the emission rates of H I- and He I-ionizing photons are not significantly affected. The mass-loss rates assumed in our models were chosen to smoothly connect the mass-loss rates of subdwarfs (Krtička et al. 2016) with the observed mass-loss rates of WR stars (Nugis & Lamers 2000). Our assumed mass-loss rates also match well with the observed mass-loss rate of the stripped star in the binary system HD 45166 (Groh et al. 2008). The recent theoretical predictions by Vink (2017) suggest that the mass-loss rates of stripped stars may be ten times lower than what we assume in this paper. The winds of stripped stars are likely not reaching close to the Eddington limit, in contrary to massive main-sequence and WR stars (cf. Bestenlehner et al. 2014). This suggests that the wind mass-loss rate from stripped stars is lower than that from WR stars and thus not well-described by the recipe for WR stars of Nugis & Lamers (2000). To establish which are the wind mass-loss rates from stripped stars, observations of a sample of stripped stars are necessary. If, as suggested by Vink (2017), the mass-loss rates from stripped stars indeed are lower than what the recipe from Nugis & Lamers (2000) predicts, it would likely imply an increase of the emission rates of He II-ionizing photons presented in this work. The emission rates of H I- and He I-ionizing photons are robust against wind uncertainties.
Citation: Groh et al. 2008; Groh et al. 2008
Citation function: Background; Background