adsabs/AstroCLIMB huggingface dataset:
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: The correlation between spectral lag and peak luminosity in the rest frame. The GRB sample is collected from the previous statistical investigations (Li et al. 2004; Gehrels et al. 2006; Ukwatta et al. 2012; Goldstein et al. 2017; Xiao et al. 2022a; Lü et al. 2022). The GRB 221009A for spectral lag of the two unsaturated main emission episodes (T 0 + 180 s ∼T 0 + 210 s, T 0 + 280 s ∼T 0 + 350 s) and other energetic GRBs are highlighted by red star, blue star, and green circles, respectively. The blue shaded region represents the common intrinsic spectral lags (3σ) calculated from 46 sGRBs with redshift measurements (Xiao et al. 2022a).
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: Left panel: the X-ray luminosity light curves of energetic GRBs in our sample. The blue circles and red circles represent the GRB 130427A and GRB 221009A, respectively. The gray background circles show that other energetic GRBs. Right panel: the distribution of decay slope in X-ray late phase for energetic GRBs in our sample, and the red vertical line represent GRB 221009A location.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption:  E p,i and E  γ,iso correlation diagram with known redshift data (Zhang et al. 2009; Qin & Chen 2013; Zou et al. 2018; Minaev & Pozanenko 2020; Jia et al. 2022). The orange and blue solid lines represent the best-fit correlations for lGRBs and sGRBs, respectively. The blue circles, orange circles, blue star, red star, and green circles represent the sGRBs, lGRBs, GRB 130427A, GRB 221009A, and other energetic lGRBs, respectively.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: lGRB/sGRB classification diagram in the T 90 − E p domain. The blue circles, orange circles, blue star, red star, and green circles represent the sGRBs, lGRBs, GRB 130427A, GRB 221009A, and other energetic GRBs, respectively. The red vertical dotted line represent T 90 = 2 s. The histograms of top and right represent the T 90 and E p distribution for all GRBs, and the blue and red vertical lines represent GRB 130427A and GRB 221009A location, respectively.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: Cumulative distribution of GRB isotropic luminosity for total samples in the different redshift bins. The blue circles show the observed distribution, and the red solid lines, red dashed lines, and blue solid lines represent the best-fitting CPL model, BPL model, and PL model, respectively.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: The diagram in the ﹩{T}_{90}-{\rm{\Delta }}{t}_{\min }﹩ domain. The samples of sGRBs (blue circles) and lGRBs (orange circles) are from Golkhou et al. (2015). The red arrow and green circles represent GRB 221009A and other energetic GRBs, respectively. The red vertical dotted line represent T 90 = 2 s.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: Galactic extinction corrected R band afterglow light curves. The gray background lines show that we collected optical data, and the green lines show the optical afterglow of energetic GRBs in our sample. The blue star line and red star line represent the GRB 130427A and GRB 221009A, respectively.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: Comparison between the normalized best-fitting CPL model of the total-redshift bin (0 < z < 9.4) and the best-fitting CPL model of the low-redshift bin (0 < z < 0.5) for total samples (left panel) and pure Konus-Wind samples (right panel). The blue dashed lines and red solid lines represent the best-fitting CPL model for total-redshift bin and low-redshift bin.
Paper: GRB 221009A: An Ordinary Nearby GRB with Extraordinary Observational Properties
Authors: Lin Lan, He Gao, An Li, Shuo Xiao, Shunke Ai, Zong-Kai Peng, Long Li, Chen-Yu Wang, Nan Xu, Shijie Lin, Wei-Hua Lei, Bing Zhang, Yan-Qiu Zhang, Chao Zheng, Jia-Cong Liu, Wang-Chen Xue, Chen-Wei Wang, Wen-Jun Tan, Shao-Lin Xiong
Figure caption: The likehood distribution of α, β and E b in the BPL model, from the total samples (left panel) and pure Konus-Wind samples (right panel) in different redshift bins.
Paper: Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Authors: Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur
Figure caption: The analysis of a gravitational lensing signal with noise taken from HST data. The data are shown on the top panel (in log10). The gray contours show samples generated with SLIC and the dark blue contours show biased results when a Gaussian likelihood is sampled with an MCMC initialized at the true parameter values.
Paper: Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Authors: Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur
Figure caption: Top row: examples of noise patches from the data sets. Bottom row: examples of noise patches generated using the score models used in this work. These examples are generated by solving the reverse-time variance-exploding stochastic differential equation with 1000 steps of the discretized Euler–Maruyama solver.
Paper: Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Authors: Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur
Figure caption: Same as Figure 2 but performed on JWST data (image is in linear intensity units).
Paper: Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with Non-Gaussian Noise
Authors: Ronan Legin, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur
Figure caption: The coverage probability calculated by estimating the SLIC likelihoods for 200 different realizations of HST noise for a signal similar to that of Figure 2. The 3σ error region (shaded) is calculated using jackknifing. The trend of following the dashed diagonal line shows that the likelihood is accurate (unbiased).
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: The top panels show 2D slices of the current density, plasma beta (β), and temperature. Zoom-ins on three regions with plasmoids are shown in the surrounding panels. In the zoom-in panels, the in-plane magnetic fields are shown on top of the β map, and the velocity vectors are shown on top of the temperature map. The three zoom-in regions are the same as the subvolumes shown in Figure 1 and in the same order. The zoom-ins are examples of the three different types of regions in which reconnection events that lead to plasmoid formation occur in our simulation: fully cold (left), interface of warm and cold (middle), and fully warm (right). For each of the zoom-ins, we show profiles perpendicular to the prominent current sheet of the magnetic field components perpendicular (B rec; this is the component that reconnects) and parallel (B guide) to the local guide field, the velocities into and along the current sheet (v rec and v shear, respectively) normalized by one-tenth of the local Alfvén velocity (0.1v A), and the log of the temperature ﹩{\mathrm{log}}_{10}\,(T/{T}_{0})﹩. The current sheets all show a change in direction of B rec, a reconnection speed of v rec ∼ 0.01v A, and some compression of B guide in the cases that have any appreciable guide field. In all cases, v shear < v A, so they should be stable to the Kelvin–Helmholtz instability. An animated version can be found here: https://dfielding14.github.io/movies/.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Summary of the magnetothermal phase structure. The top left and right panels show the joint thermal pressure–temperature phase distribution colored by the average plasma beta (β) and thermal pressure–magnetic pressure phase distribution colored by the average temperature, respectively. The contours trace the fraction of the total mass per logarithmic bin spaced by 1 dex from 10−4 to 1 going from purple to white. The thermal equilibrium curve (Γ = ρΛ) is shown by the black dotted line in the top left panel, and lines of constant β are shown by the colored dotted lines in the top right panel. The mass- (f  M ; solid lines) and volume- (﹩{f}_{{ \mathcal V }}﹩; dashed lines) weighted temperature distributions are shown in the middle left panel for the full volume (black lines) and the high-curvature regions only (K  B  > 512/L;  purple lines). The mass-weighted temperature distributions of the high-curvature regions in the example plasmoid subvolumes (same as in Figures 1 and 2) are shown in the bottom left panel. The volume distributions of the magnetic curvature of the total volume (black), cold gas (blue), and warm gas (yellow) are shown in the middle right panel. The dotted line shows the curvature distribution for the isothermal simulation. The curvature distributions in the plasmoid subvolumes are shown in the bottom right panel.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: (Top) Volume rendering of the temperature (opaque blue corresponds to T ≈ T 0/100, and transparent yellow corresponds to T ≈ 10T 0). Uniformly seeded magnetic field lines are shown in purple, many exhibiting helical patterns that trace plasmoids. (Bottom) Volume rendering of the electric current (increasing from transparent cyan to opaque magenta) and magnetic fields (randomly colored) in three subvolumes containing plasmoids and X-points that are L/8 on a side. They are shown in full volume as colored cubes (left: blue; middle: green; right: red) and respectively exemplify cold, asymmetric, and hot current sheets undergoing plasmoid-mediated reconnection. A rotating movie of these can be found here: https://dfielding14.github.io/movies/.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Statistical measures of the scale-dependent magnetic field and velocity structure. The left column shows the results from our turbulent thermal instability simulations, and the right column shows the results from the isothermal turbulence simulation. The top panels show the magnetic M(k) (orange) and velocity E(k) (blue) power spectra compensated by k 5/3. Approximate power-law fits are shown by the thin dotted lines. Also shown in the top left panel is the velocity spectrum from the nonmagnetized simulation (green). The middle row shows the second-order magnetic and velocity structure functions parallel and perpendicular to the local mean field. The bottom row shows the anisotropy of the magnetic and velocity fluctuations defined to be the scale at which the parallel and perpendicular structure functions are equal.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Distribution and properties of the cold clumps in our fiducial turbulent thermally unstable simulation. Clumps are defined to be contiguous regions of material on the cold stable thermal equilibrium. The left panels show the distribution of clump masses. The top right panel shows the internal velocity dispersion as a function of clump size (given by the cube root of the clump volume ﹩{r}_{\mathrm{cl}}={{ \mathcal V }}_{\mathrm{cl}}﹩) in pink and the pairwise velocity difference of the clumps as a function of separation (ℓ) in blue. The solid lines show the median values. The bottom right panel shows the clump elongation vs. clump size, with larger values indicating highly elongated clumps, and values of ∼1–2 indicating nearly spherical objects.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Comparison of the velocity and magnetic field spectra for turbulent thermal instability simulations with resolutions ranging from L/Δx = 2048 to 128. The approximate power-law fits to the highest-resolution simulation are repeated in each panel to ease comparison.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Comparison of the temperature (top) and current (bottom) in 2D simulations with either no explicit dissipation processes and a resolution of Δx/L = 1/2048 (left) or small but fully resolved dissipation processes and a resolution of Δx/L = 1/8192 (right). Also shown are zoom-ins onto regions of 1/16 of the domain centered on tearing unstable current sheets forming cold plasmoids. On both large and small scales, there is a marked similarity between these two simulations, indicating that numerical dissipation in simulations with high enough resolution can enable plasmoid-mediated reconnection in a manner qualitatively consistent with resolved simulations with explicit dissipation.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: The top panel shows the vorticity distribution in bins of current. The black dashed line shows a one-to-one relation. This demonstrates that the vorticity varies only slightly over a wide range of current values. The bottom panels show 2D slices of magnitude of the vorticity (left) and current (right). The slices are at the same location as in Figure 2. There are clear current sheets with small vorticity and some with appreciable vorticity. The plasmoid examples highlighted in Figure 2 exhibit a range of vorticity values ranging from negligible to appreciable.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: The 2D slices of current and temperature from simulations with the same parameters as our fiducial simulation but with lower resolution ranging from Δx/L = 1/128 to 1/2048. The current is normalized by j 0 = 512 B 0. A multiphase medium clearly develops, as do large current sheets; however, the current sheets in simulations with worse resolution do not show clear signs of the plasmoid instability because of the large numerical dissipation.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Velocity and magnetic field power spectra at eight logarithmically spaced times in a turbulent thermally unstable simulation with Δx = L/1024. The spectra reach a steady configuration by ∼1.5t eddy.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Curvature distribution at eight logarithmically spaced times in a turbulent thermally unstable simulation with Δx = L/1024. The distribution reaches a steady configuration by ∼1.5t eddy.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: Comparison of the temperature and curvature distributions for turbulent thermal instability simulations with resolutions ranging from L/Δx = 2048 to 128.
Paper: Plasmoid Instability in the Multiphase Interstellar Medium
Authors: Drummond B. Fielding, Bart Ripperda, Alexander A. Philippov
Figure caption: (Top) The black line shows the cooling function, and the colored lines show the heating function for a range of pressures. (Bottom) Equilibrium pressure for these cooling and heating functions vs. temperature.
Paper: Exploring Proxies for the Supermassive Black Hole Mass Function: Implications for Pulsar Timing Arrays
Authors: Joseph Simon
Figure caption: 2D differential contribution to ﹩{A}_{\mathrm{yr}}^{2}﹩ from two different aspects of the simulated binary SMBH population: binary chirp mass (M  c ) shown on the x-axis and redshift shown on the y-axis. The columns show the VDF inference on the left (in blue) and the GSMF inference on the right (in orange). The rows represent two different SMBH—host galaxy relations with MM13 on the top and KH13 on the bottom. The limits of the color bars are the same in all cases. The biggest difference between the VDF and GSMF is the contribution to the signal at higher redshift (z > 0.5). In the case of the GSMF inference, the binaries that contribute significantly to the GWB are contained to the local universe, while the VDF inference shows significant contributions all the way out to z ∼ 1 and beyond. One can infer from this that the underlying SMBH mass function implied by the VDF includes more massive systems at higher redshifts then that implied by the GSMF.
Paper: Exploring Proxies for the Supermassive Black Hole Mass Function: Implications for Pulsar Timing Arrays
Authors: Joseph Simon
Figure caption:  A yr predictions calculated using the VDF (solid) and GSMF (dotted) methods to infer the SMBH mass function. The constraints from both the NANOGrav 12.5 year data set (Arzoumanian et al. 2020) and IPTA DR2 (Antoniadis et al. 2022) are shown above the two histograms. Each histogram includes 3000 predictions, with 1000 coming from each of the three SMBH—host galaxy relations used. The 90% interval of the VDF predictions cover 0.24 dex with a mean at log10 A yr = −14.74, while the GSMF predictions cover 0.4 dex over the 90% interval with a mean at log10 A yr = −14.9. Both methods are able to reproduce the emerging signal, but the VDF predictions tend toward higher amplitudes than the GSMF predictions.
Paper: Exploring Proxies for the Supermassive Black Hole Mass Function: Implications for Pulsar Timing Arrays
Authors: Joseph Simon
Figure caption:  A yr predictions calculated using the VDF (solid) and GSMF (dashed) methods to infer the SMBH mass function. Each panel shows the predictions for a different SMBH—host galaxy relation: KH13 is shown in blue in the left panel, MM13 is shown in orange in the center panel, and dN19 is shown in green in the right panel. Additionally, in all three panels, the full distribution of predictions from Figure 1 for all three relations is shown in gray. The VDF predictions appear to not be sensitive to the choice of host galaxy relation in the same way that the GSMF predictions are, and as seen in Figure 1 the VDF predictions are always at a higher amplitude. However, the magnitude of the difference between the VDF and GSMF predictions is dependent on the specific host galaxy relation chosen. The largest difference is seen when using MM13, and the smallest difference is seen when using KH13.
Paper: Exploring Proxies for the Supermassive Black Hole Mass Function: Implications for Pulsar Timing Arrays
Authors: Joseph Simon
Figure caption: Redshift distribution of individual binary SMBH systems at f GW = 8 nHz averaged over 100 different population realizations. On average, the VDF method (blue curve) predicts more than twice as many potential individual systems as the GSMF method (orange curve). These systems are expected to be detectable for the IPTA by 2025 (Xin et al. 2021). As seen in previous work, the GSMF method predicts that single sources will be found in the local universe (z < 0.5; Rosado et al. 2015). In contrast, the VDF method shows only a slight preference for systems at lower redshift. As seen in Figure 3, the larger-mass systems at higher redshifts that are present in the VDF predictions directly translate to an increase in individually resolvable sources in PTA data for z > 0.5.
Paper: Exploring Proxies for the Supermassive Black Hole Mass Function: Implications for Pulsar Timing Arrays
Authors: Joseph Simon
Figure caption:  A yr predictions calculated using the inferred VDF (﹩{\sigma }_{\inf }﹩, black line) and the spectroscopic VDF (σ spec, red line) to infer the SMBH mass function. In the case of the σ spec predictions, spectroscopic data from SDSS (Bernardi et al. 2010) and the LEGA-C survey (Taylor et al. 2022) were used at z < 1, while the inferred VDF was used to fill in from 1 < z < 1.5. Given the consistency that the LEGA-C survey finds between the spectroscopic and inferred VDFs (Taylor et al. 2022), it is not surprising that these results are so consistent. While it would be preferred to use only spectroscopic VDFs for this work, the similarity in these results show that the inferred VDFs are an acceptable alternative.
Paper: Detection of X-Ray Polarized Emission and Accretion-disk Winds with IXPE and NICER in the Black Hole X-Ray Binary 4U 1630−47
Authors: Divya Rawat, Akash Garg, Mariano Méndez
Figure caption: Results of the simultaneous fits to the NICER spectra (top left panel) and IXPE Stokes spectra, Q (middle left panel), and U (bottom left panel) of 4U 1630−47 fitted with model POLPOW*TBABS*ZXIPCF  1  *ZXIPCF  1  *DISKBB. The right panels show the respective residuals of the best-fitting model to the data. The best-fitting parameters are given in Table 2.
Paper: Detection of X-Ray Polarized Emission and Accretion-disk Winds with IXPE and NICER in the Black Hole X-Ray Binary 4U 1630−47
Authors: Divya Rawat, Akash Garg, Mariano Méndez
Figure caption: The polarization angle, PA (upper left), polarization degree, PD (upper right), and normalized Stokes parameters, Q/I (lower left) and U/I (lower right), of 4U 1630−47 as a function of energy.
Paper: Detection of X-Ray Polarized Emission and Accretion-disk Winds with IXPE and NICER in the Black Hole X-Ray Binary 4U 1630−47
Authors: Divya Rawat, Akash Garg, Mariano Méndez
Figure caption: Upper panel: NICER light curve of 4U 1630−47 in the 0.2–12.0 keV band. The shaded area represents the simultaneous IXPE observation of the source. The bin size of the NICER light curve is 100 s. Lower panel: hardness−intensity diagram of 4U 1630−47 using NICER data.