adsabs/AstroCLIMB huggingface dataset:
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Outflow movement of gas clumps. Clumps closer to the bicone axis are more highly ionized. In a Seyfert 2 configuration, the projected velocity (i.e., the velocity observed by us) is indeed more blueshifted for species further away from the axis, because the velocity component toward the observer is larger.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Correlation of the dust covering factor with the high-ionization line vs. X-ray 14–195 keV flux. The IR (dust) covering factor values are taken from Ichikawa et al. (2019). We use the Linmix module to fit a linear regression to the data. The package can take upper limits along one axis into account when performing a linear regression using MCMC. For the fit, we exclude the upper limits in the covering factor (x-axis), amounting to five dismissed points for [Si VI] λ1.9640 (left) and four for [Si X] λ1.4300 (left). We include the flux ratio (y-axis) upper limits. The shaded regions show the pointwise 1σ, 2σ, and 3σ credibility intervals of the linear regression fit from Linmix.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Compilation of the various fitting regions separated into the individual spectral regions. The spectrum of 2MASX J214805.31-535941.3 (BAT ID 1604) is shown. For this source, the emission lines are fitted well. The vertical dashed lines indicate the location of the nonsystemic corrected emission line.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption:  Ratio between FWHMs of Paschen and Hα lines as a function of the line-of-sight X-ray hydrogen column density.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Compilation of the various fitting regions separated into the individual spectral regions. The spectrum of ESO 103-035 (BAT ID 988) is shown. For this source, the S/N is lower and for the Paα spectral region, spline fitting for continuum estimation is applied. The vertical dashed lines indicate the location of the nonsystemic corrected emission line.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Compilation of a selection of 12 spectra from the sample investigated and presented in this study. The spectra have been corrected for telluric absorption. Regions that are potentially heavily affected by telluric absorption even after correction are indicated in orange.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Correlation between the CL luminosities and the mass of the central black hole. Only moderate correlations are found. R pear is the Pearson correlation and p the Pearson p-value.
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Example of hidden broad lines: the Paα emission line in the NIR regime clearly shows a broad component, while the Hβ and Hα emission lines in the optical regime do not show clear broad components. The source shown is LEDA 157443 (BAT ID 597).
Paper: BASS. XXVIII. Near-infrared Data Release 2: High-ionization and Broad Lines in Active Galactic Nuclei 
Authors: Jakob S. den Brok, Michael J. Koss, Benny Trakhtenbrot, Daniel Stern, Sebastiano Cantalupo, Isabella Lamperti, Federica Ricci, Claudio Ricci, Kyuseok Oh, Franz E. Bauer, Rogerio Riffel, Alberto Rodríguez-Ardila, Rudolf Bär, Fiona Harrison, Kohei Ichikawa, Julian E. Mejía-Restrepo, Richard Mushotzky, Meredith C. Powell, Rozenn Boissay-Malaquin, Marko Stalevski, Ezequiel Treister, C. Megan Urry, Sylvain Veilleux
Figure caption: Comparison of the two continuum fitting methods. The black spectrum in the upper part shows the raw spectrum. The spline fit is indicated in blue and the fourth-order polynomial fit in brown. The fourth-order polynomial fit corrected spectrum is shown in the center (in brown) and the spline fit corrected spectrum is shown at the bottom (in blue). The red shaded regions are excluded from the continuum fit due to intrinsic emission.
Paper: Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data
Authors: Varad Deshmukh, Natasha Flyer, Kiera van der Sande, Thomas Berger
Figure caption: Performance comparison between CNN-Only and CNN+ERT models across six different metrics on the testing set. For each metric box plot, the 10 dots shown represent the individual score of each of the 10 data set splits. The colored region in each box plot is the 25%–75% quartile range, the horizontal line is the mean, and the error bars span the range of the 10 data set split results.
Paper: Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data
Authors: Varad Deshmukh, Natasha Flyer, Kiera van der Sande, Thomas Berger
Figure caption: Hyperparameter tuning across multiple seeds for both stages of the hybrid flare prediction model. In both stages, hyperparameters that optimize the TSSscaled metric are determined. Each colored line shows the results for one random seed in the train/validate/test data set splitting.
Paper: Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data
Authors: Varad Deshmukh, Natasha Flyer, Kiera van der Sande, Thomas Berger
Figure caption: Feature ranking using the Gini impurity index from the ERT model.
Paper: Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data
Authors: Varad Deshmukh, Natasha Flyer, Kiera van der Sande, Thomas Berger
Figure caption: The VGG-16 architecture outline, as adapted for the solar flare prediction problem. In this figure, the input to the VGG-16 model is a temporal stack of four magnetograms. For other experiments, this is changed accordingly. The output of the model is two complementary neurons representing the flaring and nonflaring probability of the input sample.
Paper: Decreasing False-alarm Rates in CNN-based Solar Flare Prediction Using SDO/HMI Data
Authors: Varad Deshmukh, Natasha Flyer, Kiera van der Sande, Thomas Berger
Figure caption: Our two-stage model for solar flare prediction. The input is a temporal stack of B r  magnetograms from SDO/HMI which is both fed to a custom CNN model and analyzed for feature vectors. The CNN model outputs the probability of flaring with the 12 hr forecast window and this probability is combined with the feature vectors to create a single feature vector input to the ERT model. The output of the ERT model is a binary event prediction.
Paper: New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data
Authors: Zhihong He, 治宏 何, Chunyan Li, 春燕 李, Jing Zhong, 靖 钟, Guimei Liu, 桂梅 刘, Leya Bai, 乐娅 白, Songmei Qin, 松梅 秦, Yueyue Jiang, 悦悦 蒋, Xi Zhang, 茜 张, Li Chen, 力 陈
Figure caption: Metallicity gradient (upper panels, taking solar metallicity from Asplund et al. 2005) for the new OC candidates and their distribution within the Galactic plane. In the lower panels, the colored dots denote their initial metal fraction. The panels show metallicity for different age ranges, left: >50 Myr, right: ≤50 Myr. The blue dashed lines in the upper panels show the liner fittings for median [M/H] values, the error bars show the standard deviation for [M/H] values in the corresponding radius bin (≥10 candidates in each bin), and the colored lines demonstrate the metallicity gradients from the literature (Magrini et al. 2009; Spina et al. 2021). The dashed lines in the lower panels portray the Galactocentric radius, from 6–11 kpc, with an interval of 1 kpc.
Paper: New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data
Authors: Zhihong He, 治宏 何, Chunyan Li, 春燕 李, Jing Zhong, 靖 钟, Guimei Liu, 桂梅 刘, Leya Bai, 乐娅 白, Songmei Qin, 松梅 秦, Yueyue Jiang, 悦悦 蒋, Xi Zhang, 茜 张, Li Chen, 力 陈
Figure caption: Left: histograms of the parallaxes of cataloged Gaia DR2 OCs (including those of Castro-Ginard et al. 2018; Cantat-Gaudin et al. 2018; Castro-Ginard et al. 2019; Cantat-Gaudin et al. 2019; Ferreira et al. 2019; Liu & Pang 2019; Sim et al. 2019; Castro-Ginard et al. 2020; Ferreira et al. 2020; Qin et al. 2021; Casado 2021; Ferreira et al. 2021; He et al. 2021a; Hunt & Reffert 2021) clusters and new OC candidates, as well as the result of adding them together. Right: proportion of increase in the number of Gaia DR2 OCs at different parallaxes.
Paper: New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data
Authors: Zhihong He, 治宏 何, Chunyan Li, 春燕 李, Jing Zhong, 靖 钟, Guimei Liu, 桂梅 刘, Leya Bai, 乐娅 白, Songmei Qin, 松梅 秦, Yueyue Jiang, 悦悦 蒋, Xi Zhang, 茜 张, Li Chen, 力 陈
Figure caption: Locations of the new OC candidates (red dots) and known OCs (gray dots) determined by the median parallax of member stars. (a) Image viewed from the north Galactic pole from which the galaxy rotates clockwise. The colored lines show the spiral arms determined by Reid et al. (2019). (b) Same as (a) but with an edge-on view. The black plus signs represent new candidates older than 109 yr. Here, we have assumed the Reid et al. (2019) recommended values of R 0 = 8.15 kpc and Z 0 = 5.5 pc.
Paper: New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data
Authors: Zhihong He, 治宏 何, Chunyan Li, 春燕 李, Jing Zhong, 靖 钟, Guimei Liu, 桂梅 刘, Leya Bai, 乐娅 白, Songmei Qin, 松梅 秦, Yueyue Jiang, 悦悦 蒋, Xi Zhang, 茜 张, Li Chen, 力 陈
Figure caption: Total observed proper-motion dispersion using Gaia EDR3 vs. parallax. The x-axis is a log scale for known OCs from the homogeneous OC catalog of CG20 (black dots) compared with the new candidates in this work (red dots).
Paper: New Open-cluster Candidates Found in the Galactic Disk Using Gaia DR2/EDR3 Data
Authors: Zhihong He, 治宏 何, Chunyan Li, 春燕 李, Jing Zhong, 靖 钟, Guimei Liu, 桂梅 刘, Leya Bai, 乐娅 白, Songmei Qin, 松梅 秦, Yueyue Jiang, 悦悦 蒋, Xi Zhang, 茜 张, Li Chen, 力 陈
Figure caption: Histograms of the cluster ages (left), G-band extinctions (right) in CG20, and the values derived from isochrone fittings in this work.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: The d LF ratios as a function of reduced sample size n  r  for the step (3), ﹩{\hat{\phi }}_{1{\rm{d}}}﹩ vs. ﹩{\hat{\phi }}_{2{\rm{d}}}﹩. The cyan dashed line indicates the boundary where the ratio is equal to 1, and the red solid circles mark the points that meet none of conditions (1)–(5) in Section 4.1. The black dashed–dotted lines mark the critical n  r  of 320 and 1000. The gray thick solid line is the power-law fitting of 10.96n  r  −0.38, and the orange shaded area takes into account the 3σ error band.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: LF estimates from z = 0.6 to 2.2. The blue curves show our fiducial LF given by ﹩{\hat{\phi }}_{{\rm{a}}}﹩. The shaded regions show the 3σ (99.93 %) uncertainties. The red solid circles indicate the binned LFs inferred by Kulkarni et al. (2019). The red open circles indicate magnitude bins excluded in their paper due to incompleteness in the data set.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Importing kdeLF and its subroutines to illustrate the overall structure of kdeLF.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Posterior distributions of the bandwidth parameters for ﹩\hat{\phi }﹩ (left) and ﹩{\hat{\phi }}_{{\rm{a}}}﹩ (right) in the 0.6 < z < 0.8 bin. The vertical lines mark the locations of best-fit values and 1σ errors by MCMC.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: A sketch illustrating the faint luminosity limits of the LFs estimated by ﹩{\hat{\phi }}_{{\rm{a}}}﹩ and ﹩{\hat{\phi }}_{\mathrm{bin}}﹩.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Example of code to run kdeLF for the 0.6 < z < 0.8 data.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Distributions of d LF in eight redshift bins for different LF estimators based on 200 simulated samples. The red solid curves represent ﹩{\hat{\phi }}_{{\rm{a}}}﹩ with no dividing redshift bins. The black solid and dashed curves represent the results of uniform L bin ΔL = 0.3 and variable L bins ﹩{\rm{\Delta }}L\propto 1/{\mathrm{log}}_{10}(n)﹩, respectively, for ﹩{\hat{\phi }}_{\mathrm{bin}}﹩. The blue solid curves show the results of ﹩{\hat{\phi }}_{\mathrm{auto}}﹩, which represents the optimal one chosen by the Kolmogorov–Smirnov test criterion among ﹩\hat{\phi }﹩, ﹩{\hat{\phi }}_{{\rm{a}}}﹩, ﹩{\hat{\phi }}_{1}﹩, and ﹩{\hat{\phi }}_{1{\rm{a}}}﹩.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: The relations between d LF ratios and D KS ratios for the first two steps, ﹩\hat{\phi }﹩ vs. ﹩{\hat{\phi }}_{{\rm{a}}}﹩, and ﹩{\hat{\phi }}_{1}﹩ vs. ﹩{\hat{\phi }}_{1{\rm{a}}}﹩. In each panel the cyan dashed lines indicate the boundary where the ratio is equal to 1. The red solid circles mark the points that meet none of conditions (1)–(4) in Section 4.1.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Distributions of d LF in eight redshift bins for different LF estimators based on 200 simulated samples. In each redshift bin, the black solid curve represents the ﹩{\hat{\phi }}_{\mathrm{bin}}﹩ estimator of Page & Carrera (2000). A uniform L bin of ΔL = 0.3 is used for ﹩{\hat{\phi }}_{\mathrm{bin}}﹩. The red dashed and solid curves respectively correspond to the ﹩\hat{\phi }﹩ and ﹩{\hat{\phi }}_{{\rm{a}}}﹩ estimators; the cyan dashed and solid curves respectively represent the ﹩{\hat{\phi }}_{1}﹩ and ﹩{\hat{\phi }}_{1{\rm{a}}}﹩ estimators.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption:  L–z plane showing the domain of the data set.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Top panel: an example data set with the bounded domain of ﹩\{{Z}_{1}\lt z\lt {Z}_{2},\,L\gt {f}_{\mathrm{lim}}(z)\}﹩. Bottom panel: what the data in the top panel look like after transformation (light gray points) by Equation (7), and adding the reflection points (red points).
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: The statistic d LF changes with the sample size n for ﹩\hat{\phi }﹩, ﹩{\hat{\phi }}_{\mathrm{bin}}﹩, and ﹩{\hat{\phi }}_{1}﹩ based on 200 simulated samples. Each sample is divided into eight redshift bins (edges of 0.0, 0.2, 0.5, 1.0, 1.7, 2.5, 3.5, 4.5, and 6.0), thus there are 1600 d LF value points in each panel. Variable L bins of ﹩{\rm{\Delta }}L\propto 1/{\mathrm{log}}_{10}(n)﹩ are used for ﹩{\hat{\phi }}_{\mathrm{bin}}﹩.
Paper: A Flexible Method for Estimating Luminosity Functions via Kernel Density Estimation. II. Generalization and Python Implementation
Authors: Zunli Yuan, Xibin Zhang, Jiancheng Wang, Xiangming Cheng, Wenjie Wang
Figure caption: Ratios of ﹩{d}_{\mathrm{LF}}(\hat{\phi })﹩ to ﹩{d}_{\mathrm{LF}}({\hat{\phi }}_{{\rm{a}}})﹩ as a function of sample sizes n for the 200 simulated samples. The cyan dashed line indicates the boundary where the ratio is equal to 1. About 20% of points are below the boundary.
Paper: NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected by the Swift/Burst Alert Telescope All-sky Hard X-Ray Survey
Authors: Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Satoshi Yamada, Claudio Ricci
Figure caption: Normalized distribution of ﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{Equ}}/{\mathrm{cm}}^{-2}﹩. The blue histograms represent the ﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{Equ}}/{\mathrm{cm}}^{-2}﹩ of unobscured AGNs and Compton-thin AGNs, and the orange histograms show the ﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{Equ}}/{\mathrm{cm}}^{-2}﹩ of Compton-thick AGNs. Here we consider 29 CTAGNs (28 objects in this work and the Circinus Galaxy) and 40 less obscured AGNs (24 objects in this work and 16 objects in Ogawa et al. 2021). We note that Compton-thin AGNs represent objects whose logarithmic hydrogen column density along the line of sight (﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{LOS}}/{\mathrm{cm}}^{-2}﹩) are smaller than 24 and unobscured AGNs mean the objects whose ﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{Equ}}/{\mathrm{cm}}^{-2}﹩ are smaller than 22.
Paper: NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected by the Swift/Burst Alert Telescope All-sky Hard X-Ray Survey
Authors: Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Satoshi Yamada, Claudio Ricci
Figure caption: (a) The distribution of the torus angular width (σ). (b) The distribution of the Compton-thin torus covering factor (C 22). (c) The distribution of the Compton-thick torus covering factor (C 24). (d) The distribution of the logarithmic hydrogen column density along the equatorial direction (﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{Equ}}/{\mathrm{cm}}^{-2}﹩)
Paper: NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected by the Swift/Burst Alert Telescope All-sky Hard X-Ray Survey
Authors: Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Satoshi Yamada, Claudio Ricci
Figure caption: The distribution of the hydrogen column density along the line of sight (﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{LOS}}/{\mathrm{cm}}^{-2}﹩). Here we plot the 90% confidence upper bound of ﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{LOS}}/{\mathrm{cm}}^{-2}﹩.
Paper: NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected by the Swift/Burst Alert Telescope All-sky Hard X-Ray Survey
Authors: Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Satoshi Yamada, Claudio Ricci
Figure caption: Scatter plot of the logarithmic Eddington ratio (﹩\mathrm{log}{R}_{\mathrm{Edd}}﹩) and the logarithmic hydrogen column density along the line of sight (﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{LOS}}﹩). The blue region represents that region where the radiation pressure pushes away the obscuring material (forbidden region; Fabian et al. 2009).
Paper: NuSTAR Observations of 52 Compton-thick Active Galactic Nuclei Selected by the Swift/Burst Alert Telescope All-sky Hard X-Ray Survey
Authors: Atsushi Tanimoto, Yoshihiro Ueda, Hirokazu Odaka, Satoshi Yamada, Claudio Ricci
Figure caption: Scatter plot of the logarithmic hydrogen column density along the line of sight (﹩\mathrm{log}{N}_{{\rm{H}}}^{\mathrm{LOS}}/{\mathrm{cm}}^{-2}﹩) and the logarithmic equivalent of the Fe Kα line (logEW/eV).