adsabs/FOCAL huggingface dataset:
In order to improve the census of X-ray sources in old open clusters, we are undertaking a survey with Chandra of open clusters with ages between 3.5 and 10 Gyr. The observations are designed to reach a limiting luminosity of 





 (0.3–7 keV), or better, at the distance of the clusters. As part of this survey, we have carried out the first X-ray study of Collinder 261 (Cr 261), and we present the results of our efforts in this paper. With an estimated age of 6–7 Gyr (Bragaglia & Tosi 2006), Cr 261 is one of the oldest open clusters in the Galaxy, being superseded in age by NGC 6791 (8–9 Gyr) and Berkeley 17 (8.5–10 Gyr) only. The cluster metallicity is close to solar (Drazdauskas et al. 2016), and reported values for the distance and reddening lie between 2.2–2.7 kpc and 





, respectively (see, e.g., Gozzoli et al. 1996; Carraro et al. 1999; Bragaglia & Tosi 2006), with a higher value of the reddening considered more plausible (Friel et al. 2003). In this paper, we adopt a distance of 2.5 kpc and 





, unless stated otherwise. The latter corresponds to a V-band extinction AV = 1.05 for the canonical ratio 





, and a neutral hydrogen column density 





 (Predehl & Schmitt 1995). The Galactic coordinates of Cr 261 are 





, 





 due to its low Galactic latitude and location toward the bulge, the number of foreground and background stars projected onto the cluster is high. Cluster membership is poorly constrained for the majority of stars in the field. Cr 261 is included in the star cluster catalog of Kharchenko et al. (2013), which lists structural parameters such as the overall size of the cluster and the radius of its central region. In this work, we present an estimate for the half-mass radius rh and the approximate mass of Cr 261, which, to our knowledge, have not been reported in the literature before. These parameters facilitate a uniform comparison with the X-ray properties of other old Galactic clusters.
Citation: Bragaglia & Tosi 2006
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Citation: Bragaglia & Tosi 2006
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We determined the luminosity of the L and T components using J-, H-, and K-band bolometric corrections (Golimowski et al. 2004b; Nakajima et al. 2004; Vrba et al. 2004) valid for field dwarfs and the spectral intervals L6–L8 (Luhman 16A) and L9–T1 (Luhman 16B), which accounts for the spectral types of the pair members and their associated uncertainties. Note that the H-band bolometric correction (BC) shows the smallest dispersion among the three bands. We also used a solar bolometric luminosity of 4.73 mag and the trigonometric distance of 2.02  ±  0.02 pc determined for the system by Boffin et al. (2014). The near-infrared magnitudes of each pair member provided by Burgasser et al. (2013) and Kniazev et al. (2013) were averaged. We derived the following values: log L/L⊙ =  −4.68 ± 0.08 (L component) and −4.66  ±  0.08 dex (T component). The luminosity error bars consider the uncertainties in the photometry (typically ±0.03 mag), the distance modulus (±0.02 mag), and the corresponding BCs (typically ±0.15 mag for J and K, and ±0.08 for H). Our determination for the L component broadly agrees with Faherty et al. (2014), who found log L/L⊙ =  −4.67 ± 0.04 dex after the integration over their optical (0.6−0.9 μm) and near-infrared (0.8−2.5 μm) spectra supplemented with synthetic spectra for wavelengths longer than 2.5 μm. This supports the reliability of the BCs for these spectral types. Our luminosity determination for the T component is consistent with that of Faherty et al. (2014, −4.71 ± 0.10 at the 1σ level, although our measurement suggests a slightly brighter luminosity. In a similar manner as described in Faherty et al. (2014), we also integrated our X-Shooter spectra and found that the L and T dwarfs have nearly identical luminosity, with the T object being 0.01 dex more luminous than the L dwarf, in agreement with the luminosities inferred from the BCs. The T component has been reported to show stronger variability than the L source at optical and near-infrared wavelengths with peak-to-peak amplitudes of 11−13.5% in continuous observations and strong night-to-night evolution according to Gillon et al. (2013), Biller et al. (2013), and Burgasser et al. (2014). This might affect the luminosity determination for this particular dwarf. To account for the observed variability amplitude, an uncertainty of ±0.05 dex should be added to the error bar in the luminosity derivation of the T pair member, thus yielding log L/L⊙ =  −4.66 ± 0.13 dex. One striking result is that, despite the differing spectral classifications, both objects, Luhman 16A and B, have consistent L/L⊙ values at the 1σ level.
Citation: Faherty et al. (2014)
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Citation: Faherty et al. (2014
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Citation: Faherty et al. (2014)
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Despite the differences between the methods used to determine GC ages, they paint a similar picture: GC populations are typically older than field stars, as they mostly formed before the peak of the cosmic star formation history (z ≃ 2, Madau & Dickinson 2014), and metal-poor GCs seem to have formed coevally to or earlier than the metal-rich ones. In the Milky Way, the population of massive (M > 105 M⊙) GCs with metallicities −2.5  [Fe/H]  −0.5 is ≃12.2 Gyr old (z ∼ 4, Kruijssen et al. 2019a, based on measurements from Forbes & Bridges 2010; Dotter et al. 2010; Dotter, Sarajedini & Anderson 2011; VandenBerg et al. 2013), which is older than the inferred mean star formation time based on the star formation history of the Milky Way, τf = 10.5 ± 1.5 Gyr (Snaith et al. 2014). Despite the relatively large uncertainties (∼1 Gyr), several studies find an age–metallicity relation among the GCs in the Milky Way, with metal-poor GCs being the oldest and younger objects having higher metallicities. The exact age offset between both subpopulations depends on the catalogue considered and the metallicity range, but overall metal-poor GCs are found to be coeval to or older (by up to ∼1.25 Gyr) than the metal-rich subpopulation within the uncertainties (considering $\rm [Fe/H]\gtrless -1.2$ between the metal-poor and metal-rich subpopulations; Dotter et al. 2010; Forbes & Bridges 2010; Dotter et al. 2011; VandenBerg et al. 2013). Subsamples of GCs in different metallicity intervals are also observed to have radial age gradients, as seen in M31 (Beasley et al. 2005) and in 11 early-type galaxies from the SLUGGS survey (Forbes et al. 2015). The implied differences in formation epoch have been proposed to explain the observed differences in spatial distributions and kinematics between these metallicity GC subpopulations (Brodie & Strader 2006), and some authors also suggest they indicate different formation mechanisms (Griffen et al. 2010).
Citation: Kruijssen et al. 2019a
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The first step of the main process aims at selecting as few modes as possible to model the magnetic topology of the star, using sparse approximation. We begin by combining the sequence of observed Stokes V profiles in sliding subsets of n consecutive profiles (with n ≪ q, the total number of observed profiles) and we associate each group of profiles to the mean date over the corresponding subset. This date is then converted into a rotation cycle using the stellar rotation period, assumed to be known or derived from the GPR modelling of the longitudinal field (see Section 2.3.2). We assume that the magnetic field we want to reconstruct is not too strong, to ensure that the relation between the Stokes V data and the magnetic map remains linear. We come back in Section 5 on this limitation. Our problem thus amounts to look for the simplest linear combination of data base profiles that can reproduce the selected subset of observations to a given precision σ. In practice, this is achieved through sparse approximation (e.g. Mallat & Zhang 1993; Tibshirani 1996; Donoho & Elad 2003; Donoho, Elad & Temlyakov 2006), this problem being known as Basis Pursuit denoizing (Chen, Donoho & Saunders 1998) and formalized as follows:
(9)$$\begin{eqnarray}
\rm minimize \, \, \, \Vert \boldsymbol {X}\Vert _1 \, \, \, \rm s.t. \, \, \, \Vert \boldsymbol{ \mathsf {A}}\boldsymbol{X}-\boldsymbol{ \boldsymbol {B}}\Vert _2 \le \sigma ,
\end{eqnarray}$$where $\boldsymbol{ B}$ is a vector containing the N data points in the group of n profiles, $\boldsymbol{ \mathsf {A}}$ the dictionary, an N × p matrix for which each column contains the spectral signatures of each mode (included in the data base) taken at the observed phases, and σ the level at which we wish the data to be fitted. The components Xi of the vector $\boldsymbol {X}$ are directly associated with the modes to be reconstructed, i.e. proportional to the real or imaginary part of one of the αℓ,m, βℓ,m, and γℓ,m coefficients describing the field (up to a degree ℓmax ). Taking into account the noise in the data, the previous problem can be generalized by including an N × N diagonal matrix $\boldsymbol{ \mathsf {W}}$ containing the inverse of the errors on the spectral points. Problem (9) then becomes:
(10)$$\begin{eqnarray}
\rm minimize \, \, \, \Vert \boldsymbol {X}\Vert _1 \, \, \, \rm s.t. \, \, \, \Vert\boldsymbol{ \mathsf {W}}(\boldsymbol{ \mathsf {A}}\boldsymbol{X}- \boldsymbol {B})\Vert _2 \le \tau
\end{eqnarray}$$with τ2 being the chi-square (χ2) level at which we wish the data to be fitted.
Citation: Chen, Donoho & Saunders 1998
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CO emission is derived for each object in the catalog by converting infrared luminosity to CO line strength with the well-established LIR–





 correlation (e.g., Carilli & Walter 2013, hereafter CW13; Greve et al. 2014, hereafter G14; Dessauges-Zavadsky et al. 2015). We can then use the measured stellar mass functions of galaxies at 0  z  2 to calculate the power of CO line foregrounds, with the ability of monitoring different subsets (e.g., different stellar mass bins, quiescent versus star-forming galaxies, etc.) Now the total mean intensity of CO contamination can be expressed as
7





where 





, 3 ≤ Jupp ≤ 6, representing all CO transitions acting as foregrounds and 





 being the stellar mass function measured by the COSMOS/UltraVISTA Survey (Muzzin et al. 2013b). 





 represents an evolving mass cut that measures the depth of foreground masking (see Section 4.3 for a detailed discussion) and is set to 





 when no masking is applied. The factor 





 accounts for the mapping of frequency into distance along the line of sight (Visbal & Loeb 2010). The comoving radial distance χ, the comoving angular diameter distance DA, and the luminosity distance DL are related by 





. In the presence of scatter (as is always the case), the expectation value of a function 





 of CO luminosity at a best-fit 





 given by simstack can be written as
8





and
9





where
10





is derived from the best-fit 





 correlation, with rJ being some scaling factor for different J. Consequently, in the presence of scatter, Equation (7) becomes 





, which describes the expectation value of the total CO mean intensity, averaged over the probability distribution of 





 as specified by μ and 





. In our calculation, we consider two prescriptions: (i) CW13, who give α = 1.37 ± 0.04, β = −1.74 ± 0.40, and scaling relations appropriate for sub-millimeter galaxies which are used to convert to transitions higher than 





, and (ii) G14, who provide α and β coefficients for each individual J transition (i.e., rJ = 1) based on samples of low-z ultra-luminous infrared galaxies and high-z dusty star-forming galaxies comparable to CW13. Since the total CO foreground consists of multiple J transitions, we deem the G14 prescription more appropriate for our purposes, because it treats both the slope and intercept as free parameters when fitting to galaxies observed in different J. Henceforth, we present our results based on the G14 model unless otherwise stated.
Citation: Visbal & Loeb 2010
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We applied the convex inversion method of Kaasalainen et al. (2001) and Kaasalainen & Torppa (2001) to the optical dataset of 70 light curves (described in Appendix A and listed in Table A.1) following exactly the procedure of Hanuš et al. (2016). Specifically, we scanned rotation periods in the proximity of the expected value while testing ten initial pole solutions for each sampled period. Four poles were selected on the equator with 90° difference in longitude, and three poles in each hemisphere with the latitude ±60° and with 120° difference in longitude. We assumed that all solutions within a 3σ uncertainty interval had 




χ
2



(
1
+
3


2
/
ν


)



χ
min
2



$ \chi^2 < (1+3\sqrt{2/\nu})\,\chi^{2}_{\mathrm{min}} $



, where 




χ

min

2



$ \chi _{{\rm{min}}}^2 $


 is the χ2 of the best-fitting solution and ν is the number of degrees of freedom. This threshold to consider the solution acceptable was used before in Vokrouhlický et al. (2017) or Ďurech et al. (2018)
3 and corresponded to a ∼7% increase in 




χ

min

2



$ \chi _{{\rm{min}}}^2 $


 value. Only the best-fitting solution fulfilled the 3σ condition on the χ2. To further verify that the best-fitting solution was the only one acceptable, we also visually inspected the light-curve fit with the second-best-fitting period, similarly as in Hanuš et al. (2016), see their Figs. 3 and 4 for illustration. This solution was already inconsistent with several individual light-curves. Therefore, we considered the difference in χ2 as significant and rejected all periods except for the best-fitting one. Next, we ran the convex inversion with the unique period and multiple pole orientations (isotropically distributed on a sphere with a 30° difference) as starting points of the optimization procedure and derived a single solution within the 3σ uncertainty interval defined above. Again, we visually inspected the light-curve fit with the second-best-fitting pole orientation and rejected this solution and also considered all other solutions as non-acceptable. The final solution is given in Table 1 together with the previous determinations. It is notable that our analysis and the recent study of Kim et al. (2018) provide for the first time a unique shape and spin solution that is consistent with the preferred solution of Hanuš et al. (2016). There are two differences between our old and revised models: (i) the relative dimension along the rotation axis (or the c/a ratio) is now smaller by ∼10%, which is expected because this dimension is generally the least constrained by the optical data, and (ii) the pole directions are about 8° apart.
Citation: Vokrouhlický et al. (2017)
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We note that Mrk 590 does not fit this description. The soft excesses in obs1 and obs2 were described by a nonrotating black hole (r
in fixed at 6r

g
) and a slightly ionized accretion disk. This result contradicts recent studies of other type 1 AGNs, where the reflection model favors a rotating black hole and the inner part of the disk approaches the innermost stable circular orbit (1.25r

g
; García et al. 2019; Ghosh & Laha2020, 2021). We note a significant increase in disk ionization between observations (from 



logξ=0.52−0.30+0.77

 in obs2 to 



logξ∼3

 in obs3). We also found a significant decrease in Γ value from obs1 to obs8, indicating a spectral hardening. For obs7 and obs8, we found a well-constrained, high-energy cutoff of 



92−25+55keV

 and 



60−8+10keV

, respectively, better described by the relxill model. Although these values are relatively lower compared to other Seyfert 1 galaxies (200–300  keV; Ghosh et al. 2016; Ricci et al. 2017; Fabian et al. 2017; Akylas & Georgantopoulos 2021), similar low values of E
cut have been found in recent sample studies of Swift/BAT-selected AGNs (Kamraj et al. 2022). This low-energy cutoff may indicate a decrease in the plasma temperature of the corona only if it was higher during obs1 and obs2. But we cannot test this scenario owing to the nonavailability of data beyond 10 keV. Now hard X-ray photons illuminate the disk, so less energetic photons mean low illumination. However, we note that the disk is still moderately ionized and is capable of Ross & Fabian (2005) producing fluorescent emission lines. More importantly, we did not find any broad Fe emission line or Compton hump in any of the source spectra. The reflected flux and reflection fraction do not show statistically significant variation and remain within the 3σ value. When we plotted the soft excess flux versus the power-law flux (2–10  keV), we did not find any significant correlation (see Figure 8). This result is consistent with Boissay et al. (2016), where the shape of reflection at hard X-rays stays constant when the soft excess varies, showing an absence of a link between reflection and soft excess. In Mrk 590, the power law, the Fe line emission, and UV monochromatic flux follow the same temporal pattern (Figure 3). This result suggests that the disk and corona are most likely evolving together. However, the soft excess is not responding to this change in disk–corona properties. Hence, the soft excess emission observed in Mrk 590 is not due to ionized reflection from the disk.
Citation: Kamraj et al. 2022
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How does excitation affect 6.2 μm/7.7 μm? A harder illuminating spectrum contains more UV photons at higher energy and hence directly enhances PAH emission at shorter wavelengths relative to that at longer wavelengths (Draine et al. 2021; Rigopoulou et al. 2021). If this were true, the 11.3 μm/7.7 μm band ratio should decrease strictly with increasing excitation, because a harder radiation field preferentially enhances PAH emission at shorter wavelengths and would boost the fraction of ionized PAH molecules. However, as explained above, the variation of 11.3 μm/7.7 μm with [Ne iii]/[Ne ii] is not monotonic. Alternatively, a harder radiation field can modify PAH size distribution through preferential photo erosion of smaller PAH grains (Allain et al. 1996a; Micelotta et al. 2010a, 2010b; Murga et al. 2016). The normally narrow peak size distribution of 150 C atoms for astronomical PAHs (Weingartner & Draine 2001a; Draine & Li 2007) would shift with photo erosion toward sizes of fewer than 100 C atoms (with the total amount of smaller PAHs reduced as well). This effect also can potentially account for the increase of 6.2 μm/7.7 μm, but it cannot explain why 11.3 μm/7.7 μm does not simultaneously increase in lockstep. The 11.3 μm/7.7 μm band ratio first decreases and then increases with [Ne iii]/[Ne ii]. To account for this peculiar behavior, we propose that, during the photo erosion process, a harder radiation field first elevates the ionization fraction of the PAH molecules up to a certain threshold, which results in the decrease of 11.3 μm/7.7 μm, beyond which photo erosion proceeds to dominate by modifying the PAH size distribution, which results in the increase of 6.2 μm/7.7 μm and 11.3 μm/7.7 μm. In addition, preferential photodestruction of the ionized PAH grains, which are more vulnerable relative to the neutral ones (Allain et al. 1996b; Holm et al. 2011), also may contribute to the increase of 11.3 μm/7.7 μm. This scenario further provides a plausible explanation for the severe depletion of the overall PAH emission among the dwarf galaxies with the hardest radiation field (Figure 6(a)).
Citation: Allain et al. 1996a
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Citation: Draine et al. 2021
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Citation: Allain et al. 1996b
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Citation: Draine & Li 2007
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It has been known for more than 30 years that quasar emission lines are often shifted from the systemic velocity. Most notable is the average blueshift of the high-ionization broad C iv line (e.g., Gaskell 1982; Wilkes & Carswell 1982; Tytler & Fan 1992; Richards et al. 2002) by hundreds of 





 from systemic, with a strong dependence on luminosity (e.g., Richards et al. 2011). Low-ionization broad lines (such as Mg ii), however, are observed to have an average velocity closer to the systemic velocity (e.g., Hewett & Wild 2010). While high-ionization narrow emission lines such as [O iii] are not as blueshifted as high-ionization broad lines, they can have line peak shifts18

18
[O iii] often shows a blue asymmetry (e.g., the “blue wing”) that depends on the properties of the quasar (e.g., Heckman et al. 1981; Peterson et al. 1981; Whittle 1985; Veilleux 1991; Zhang et al. 2011; Shen & Ho 2014). The velocity offset of this wing [O iii] component appears to increase with luminosity and Eddington ratio (e.g., Zhang et al. 2011; Shen & Ho 2014), and can reach extreme values of ≳1000 





 at the highest quasar luminosities (e.g., Shen 2016; Zakamska et al. 2016). However, the velocity offset of the core [O iii] component or the [O iii] peak is generally more stable and lies within ∼50 





 of the systemic velocity, with only mild dependences on quasar luminosity and Eddington ratio (e.g., Figure 2 and Figure E2 in Shen & Ho 2014, also see, Hewett & Wild 2010).
 of tens of 





, with some extreme cases exceeding a hundred 





  (e.g., the so-called “blue outliers,” Zamanov et al. 2002; Boroson 2005; Komossa et al. 2008; Marziani et al. 2015). These average trends have been generally confirmed with recent large spectroscopic quasar samples (e.g., Vanden Berk et al. 2001; Richards et al. 2002, 2011; Shen et al. 2007, 2008, 2011; Hewett & Wild 2010; Zhang et al. 2011; Shen & Ho 2014), although differences in measuring the line center and the reference line used for systemic velocity will affect the exact value of the inferred velocity shifts.
Citation: Shen & Ho 2014
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Citation: Shen & Ho 2014
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Citation: Shen & Ho 2014
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Citation: Shen & Ho 2014
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