adsabs/AstroCLIMB huggingface dataset:
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: A re-creation of Figure 11 of N. M. McClure-Griffiths et al. (2023), with new estimates of σ  z  derived in this work overplotted. Yellow squares indicate the thickness of the molecular gas disk derived from observations of CO in emission (see Figure 6 in M. Heyer & T. M. Dame 2015, and references therein). For the Galactocentric radii probed by Y. Su et al. (2021), we separate σ  z  into the thick and thin components using dashed lines, with semitransparent yellow squares indicating σ  z  for the molecular thin and thick disks. Orange diamonds indicate the thickness of the cold H I disk derived from observations of H I in absorption (J. M. Dickey et al. 2009, 2022). The solar neighborhood estimate for the cold H I disk from J. Crovisier (1978) used by N. M. McClure-Griffiths et al. (2023) is shown as an open orange diamond. Blue squares indicate the thickness of the total H I disk derived from observations of H I in emission (HI4PI Collaboration et al. 2016). Estimates from this work (in the solar neighborhood; Table 1) are shown as magenta crosses. The result for the full ∣b∣ > 5° sample is shown as an opaque cross. We then separate σ  z  for cold H I in the solar neighborhood into the thin and thick components (corresponding to results for the τ > 0.1 and τ < 0.1 samples; Table 1) using dashed lines, with semitransparent crosses indicating σ  z  for these two samples. A black dashed line shows the exponential fit to the H I disk thickness from P. M. W. Kalberla & J. Kerp (2009), valid for Galactocentric radii 5 kpc ≲ R ≲ 35 kpc (a gray dashed line shows the extrapolated fit for R < 5 kpc). Yellow and blue dashed lines show our exponential fits to the disk thickness determined from H I emission and CO emission, respectively.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Swarm plots of the parameters derived for synthetic populations of clouds drawn randomly across the sky (left; “Control”) and synthetic populations of clouds drawn from the same (ℓ, b) distribution as the BIGHICAT background sources (right; “BIGHICAT dist.”). The x-axis indicates the variable (σ  z , σ  v , U ⊙, V ⊙, or W ⊙; small horizontal offsets are used to prevent points from overlapping), and the y-axis indicates the error in the estimated parameter, normalized by the uncertainty in the fit. We show results for 14 synthetic samples, each with different values of σ  z , ranging from 40 to 300 pc; points are colored according to the value of σ  z  for each synthetic sample.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption:  ℓ–v diagram of all 768 components in the BIGHICAT with ∣b∣ > 5°. Data are shown as filled circles. Outliers are shown as crosses. The color of each marker corresponds to the absolute value of the Galactic latitude of that component.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Top: the positions of the BIGHICAT background sources at ∣b∣ > 5°, sized according to the number of components detected along each line of sight. The background is a Mollweide projection of H I emission integrated from −75 to +75 km s−1 (HI4PI Collaboration et al. 2016). Bottom: distribution of H I optical depth (left), H I column density (middle), and H I spin temperature (right) for structures in the BIGHICAT sample with ∣b∣ > 5°. In each panel, a histogram is shown with the kernel density estimation overlaid.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Fits to σ  z  for six subsamples of the BIGHICAT sorted by H I optical depth. Results for the updated least-squares, the moment ratio, and the shape methods implemented by the kinematic_scaleheight code are shown. The x-axis value for each point is placed near the center of the optical depth range of the corresponding subsample; the x-axis value for each method is slightly offset for clarity.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Same as Figure 3, but for six subsamples of the BIGHICAT sorted by H I spin temperature.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Same as Figure 3, but for six subsamples of the BIGHICAT sorted by H I column density.
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: The relationship between the damping timescales (﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{obs}});﹩ rest-frame values) obtained from the DRW fittings and the theoretical predictions (﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{th},\ \mathrm{Kelly}+2009})﹩) of B. C. Kelly et al. (2009; i.e., Equation (1)). The cyan and orange solid dots indicate the damping timescales obtained by the zg and zr band light curves, respectively. The blue and purple dots represent the damping timescales obtained from C. J. Burke et al. (2021) and S. Zhou et al. (2024), respectively. The dark green star, diamond, and square indicate the damping timescales of Mrk 335, Zw 229-15, and WPVS 007, respectively. For more information on these sources, see Table A1 in Appendix A. The gray point (ESO 424-12 zr band) was not considered in our analysis because of its unusually small measured damping timescale (the DRW fitting result of the ESO 424-12 zr band light curve is shown in Figure B1 in Appendix B). The vertical solid gray line represents 10% of the baseline of the ZTF light curve. The solid black line indicates a one-to-one line, and the black dashed line indicates the uncertainties of 0.2 dex. The reduced χ 2 between ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{th},\ \mathrm{Kelly}+2009})﹩ and ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{obs}})﹩ is 3.86, and the Kendall’s correlation coefficient is ρ = 0.15 (p-value = 1.68 × 10−3). Most of the data points are located above the one-to-one line. That is, ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{th},\ \mathrm{Kelly}+2009})﹩ significantly underestimates the observed damping timescales.
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: The relationship between the damping timescales (﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{obs}});﹩ rest-frame values) obtained from DRW fitting and the theoretical predictions (﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{th},\ \mathrm{Zhou}+2024})﹩) of S. Zhou et al. (2024; i.e., Equation (3)). The meaning of the data points and lines in this figure is the same as in Figure 3. The triangles represent the damping timescales of the high and low states of zg and zr of the CL AGN NGC 5273, respectively. For more information on these sources, see Table A2 in Appendix A. The reduced χ 2 between ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{th},\ \mathrm{Zhou}+2024})﹩ and ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{obs}})﹩ is 1.81, and the Kendall’s correlation coefficient is ρ = 0.41 (p-value = 9.23 × 10−7). The observations and the theoretical predictions of S. Zhou et al. (2024) are roughly consistent.
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: Fitting the zg band light curve (in the observed frame) of Z 493-2 with the DRW model via the taufit code. Top panel: the solid red curve and shaded regions are the best-fitting DRW model and the 3σ confidence intervals. The gray and black data points correspond to the observations that lie outside or inside the shaded regions, respectively. Bottom-left panel: the posterior distributions of the DRW parameters obtained in our new DRW fit (i.e., the DRW fit with the black data points in the top panel). The parameter σ DRW is the long-term variability amplitude, τ DRW is the damping timescale, and σ  n  is the excess white noise amplitude. The medians of the posterior distributions are adopted as the best-fitting parameters, and the 1σ uncertainties are obtained by the 16th and 84th percentiles of the posterior distributions. The red-shaded regions in the bottom-left panel correspond to where τ DRW is greater than 10% of the baseline. Bottom-right panel: the gray curve is the PSD of the data (the black data points in the top panel), and the DRW PSD (the red solid curve) and its 1σ uncertainties (the orange shaded regions). The red-shaded regions represent the regions of frequency space not sampled by the light curve, including the regions with timescales shorter than the minimum cadence and the timescales longer than 20% of the baseline.
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: The probability distributions of the parameters a and b in the fitting relation ﹩{\mathrm{log}}_{10}({\tau }_{\mathrm{obs}})=a{\mathrm{log}}_{10}({L}_{\mathrm{bol}})+1.19{\mathrm{log}}_{10}({\lambda }_{\mathrm{rest}})+b﹩. Bottom-left panel: the joint probability distributions of a and b. Top-left panel: the distribution of a. The median value of the distribution is adopted as the best-fitting a, and the gray region is the 1σ uncertainties of a. The vertical solid orange line indicates the same parameter predicted by S. Zhou et al. (2024), which is 0.65. Bottom-right panel: the distribution of b. The median value of the distribution is adopted as the best-fitting b, and the gray region is the 1σ uncertainties of b. The vertical dashed orange line represents the same parameter predicted by S. Zhou et al. (2024), which is −30.9. Our best-fitting parameters and the theoretical predictions of S. Zhou et al. (2024) are statistically consistent.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption: Maps of gas associated with six outlying CNM components. Top panels:H I emission (white contours) overlaid on maps of dust emission. H I emission is integrated over the velocity intervals shown with horizontal dashed lines in the middle panels. Dust emission is integrated over the distance intervals shown with horizontal dashed lines in the bottom panels. Middle panels: ℓ–v diagrams of H I emission from HI4PI (HI4PI Collaboration et al. 2016). Outlying components are shown with white crosses, while nonoutlying components are shown as white circles. The H i emission is integrated over the latitude intervals shown with horizontal lines in the top panels. Bottom panels: ℓ–d diagrams from the G. Edenhofer et al. (2024) data cube. The dust emission is integrated over the latitude intervals shown with horizontal lines in the top panels.
Paper: Revisiting the Vertical Distribution of H I Absorbing Clouds in the Solar Neighborhood. II. Constraints from a Large Catalog of 21 cm Absorption Observations at High Galactic Latitudes
Authors: Daniel R. Rybarczyk, Trey V. Wenger, Snežana Stanimirović
Figure caption:  ℓ–v diagram showing components associated with flared/warped H I in the outer Galaxy, associated with the Outer Arm (E. S. Levine et al. 2006; P. M. W. Kalberla & L. Dedes 2008). White crosses indicate points identified as outliers. White circles are the nonoutlying components. The background map shows the H I emission from HI4PI (HI4PI Collaboration et al. 2016), integrated over 5° ≤ b ≤ 10° (top) and −10° ≤ b ≤ −5° (bottom).
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: The redshift distributions of the samples in this work. Left panel: the selected sources with theoretical damping timescales, which are calculated from Equation (1) (B. C. Kelly et al. 2009), smaller than 10% of the light-curve durations. The redshift ranges from 0.001 to 0.823, and the median redshift is 0.030 (the black dashed line). Right panel: the selected sources with theoretical damping timescales, which are calculated from Equation (2) (S. Zhou et al. 2024), smaller than 10% of the light-curve durations. The redshift ranges from 0.001 to 0.823, and the median redshift is 0.016 (the black dashed line).
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: Left panel: the same as Figure 3 but for sample C, which consists of sources that satisfy the criteria according to B. C. Kelly et al. (2009; i.e., Equation (1)) and S. Zhou et al. (2024; i.e., Equation (3)). Right panel: the same as Figure 4 but for sample C. Again, observations are more consistent with the theoretical predictions of S. Zhou et al. (2024) than those of B. C. Kelly et al. (2009).
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Distribution of P orb vs. M LC for detached BH–LCs detectable via photometric variability using TESS. Red circles and blue crosses represent BH–MS and BH–PMS binaries, respectively. Note the correlation between P orb and M LC, especially for the detectable BH–MS binaries.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: The ratio between the detectable binaries and intrinsic population (detection fraction) as a function of M BH for the BH–LCs detectable via photometric variations using TESS (solid) and Gaia (dashed) for the rapid (orange) and delayed (blue) models. The vertical and horizontal error bars represent the 10–90th percentiles and the bin size in M BH, respectively. The detection fraction is not strongly dependent on M BH for photometrically detectable BH–LC populations from both SNe models. The gray histogram in the top panel represents the intrinsic M BH distribution. Figure A2 illustrates that varying the SNR threshold does not significantly affect the overlap between the intrinsic and observed populations.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption:  M BH, Ecc, and M LC distributions of the present-day detached BH–LC binaries in the MW from our rapid (top) and delayed (bottom) models. The bright and faint curves represent detached BH–LCs with P orb/yr ≤ 3 and 10, respectively. The red and blue curves represent BH–MS and BH–PMS, respectively. The distributions from the rapid and delayed models show significant differences between M BH/M ⊙ = 3 and 5. BHs in this range come only from AIC of NSs in the rapid model, whereas the delayed model allows BH formation via both AIC and CCSNe in this range.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Number of BH–LC binaries resolvable via photometric variability by Gaia (left) and TESS (right) for the rapid model with SNR ≥ 10 (1). Blue, red, and green denote populations resolvable via RB, EV, and SL signals, respectively. Numbers written in white, black, and blue denote total numbers for each set, and numbers of systems with two and all three resolvable signals, respectively. For both telescopes, there are large (∼50%) overlaps between populations resolvable via EV and RB. The relatively small fraction of BH–LCs with detectable SL would also be detectable via either RB or EV or both. (The equivalent figure (Figure A1) for our delayed model is presented in the Appendix.)
Paper: How Long Will the Quasar UV/Optical Flickering Be Damped? II. The Observational Test
Authors: Guowei Ren, Shuying Zhou, Mouyuan Sun, Yongquan Xue
Figure caption: Fitting the zr band light curve (in the observed frame) of ESO 424-12 with the DRW model via the taufit code. The meaning of the data points and lines in this figure are the same as in Figure 1. There are unusually dense observations (about MJD = 58500) for ESO 424-12, which leads to the damping timescale of this light curve being unusually small, i.e., about 1 day.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Distributions of M BH and progenitor metallicity of the detached BH–LC binaries detectable through photometric variability. Red circles and blue pluses represent populations detectable using TESS and Gaia, respectively. Lines and shades in the histograms represent the median and the spread between the 10th and 90th percentiles in each bin. Left and right panels denote the rapid and delayed models.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: The reverse cumulative distribution of the expected detections of BH–LC binaries by EV (left), RB (middle), and SL (right) using TESS (red) and Gaia (blue) as a function of SNR for the rapid (top) and delayed (bottom) models. Solid and dashed lines represent the median number of detectable BH–LC binaries adopting the optimistic and pessimistic cuts (Section 3.2). The shaded regions represent the spread between the 10th and the 90th percentiles due to statistical fluctuations between our 200 independent MW realizations.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption:  P orb vs. Ecc for BH–LCs detectable via photometric variations using TESS (red circles) and Gaia (blue pluses) for our rapid (top) and delayed (bottom) models.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Detection fraction of detached BH–LC binaries as a function of P orb via Gaia’s astrometry (blue), TESS photometry (orange), and Gaia’s photometry (green) for the rapid model. Dots and error bars represent median, 10th, and 90th percentiles in each bin. Detection fraction via astrometry increases with increasing P orb until it saturates for P orb/day ≳ 102. In contrast, detection fraction from photometry exhibits a peak. At large P orb, the decrease from the peak detection fraction is due to a reduced photometric variability signal, whereas at small P orb, the decrease is due to the correlation between P orb and M LC in the BH–LC binaries (Figures 7 and 8). (The equivalent figure (Figure A3) for our delayed model is presented in the Appendix.)
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Same as Figure 7 but for BH–LCs detectable through photometric variability using Gaia.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: The reverse cumulative distribution of the RV semiamplitude for detached BH–LCs resolvable via photometric variability using Gaia (blue) and TESS (red). The solid and dashed lines denote the BH–LC binaries with G ≤ 17 and 14, respectively. Lines denote the median and the shaded regions denote the 10th–90th percentiles from statistical fluctuations. The black vertical line denotes the minimum K resolvable by Gaia.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Mass ratio (q) vs. P orb distribution for Gaia-detectable (SNR ≥ 10) simulated BH–LC binaries and Gaia DR3 detached CO–LC candidates from R. Gomel et al. (2023). Orange and blue dots represent simulated BH–LC binaries in the rapid and delayed models, while green dots represent the Gaia-observed CO–LC candidates.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: The expected numbers of detections from photometry (blue), Gaia’s astrometry (red), and Gaia’s RV (green) for the detached BH–LC population for the rapid model and their overlap. The white, black, and blue numbers denote yields in each set, and the overlap between two and three different channels, respectively. (The equivalent figure (Figure A4) for our delayed model is presented in the Appendix.)
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Same as the top panel of Figure 4 but includes BH–LC binaries with SNR ≥ 1 (solid), 5 (dashed), and 10 (dotted).
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Distribution of BH–LC binaries in the Gaia color–magnitude diagram. The gray dots in the background represents stellar sources from the Gaia archive; the simulated BH–MS and BH–PMS binaries are represented by red and blue dots, respectively.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Same as Figure 10 but for the BH–LCs in our delayed model.
Paper: Detecting Detached Black Hole Binaries through Photometric Variability
Authors: Chirag Chawla, Sourav Chatterjee, Neev Shah, Katelyn Breivik
Figure caption: Same as Figure 3 but for the BH–LCs in our delayed model.
Paper: Hydrodynamic 3D Simulation of Roche Lobe Overflow in High-mass X-Ray Binaries
Authors: David Dickson
Figure caption: Stellar interior mapped by radial velocities. For both cases, the inner boundary of the grid is mapped by the central region and the ecliptic is set in the upper right quadrant. In the ecliptic plane, the deep tidal-feeding flow structure forms in contrast to the relatively quiet poles. Deep eddy flows that emerge from the 1.1 case are further detailed by velocity vectors in the ecliptic plane. Velocities are all significantly subsonic, with sound speeds c  s  ≥ 6 × 106 cm s−1 everywhere in the stellar interior.