adsabs/AstroCLIMB huggingface dataset:
Paper: Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties
Authors: Suman Saha
Figure caption: Corner plot showing the posterior distribution of the directly estimated transit parameters from MCMC sampling for WASP-79 b.
Paper: Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties
Authors: Suman Saha
Figure caption: Same as Figure 7, but for WASP-131 b.
Paper: Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties
Authors: Suman Saha
Figure caption: Same as Figure 10, but for R  p /R ⋆.
Paper: Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties
Authors: Suman Saha
Figure caption: Same as Figure 1, but for HAT-P-17 b, HAT-P-34 b, WASP-54 b, HAT-P-13 b, and WASP-13 b.
Paper: Precise Transit Photometry Using TESS. II. Revisiting 28 Additional Transiting Systems with Updated Physical Properties
Authors: Suman Saha
Figure caption: Same as Figure 7, but for WASP-94A b.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: ODIN score distribution of various data sets with only the PS component enabled. The legend indicates that the different colored lines represent ODIN score distributions derived from distinct data sets. Both the training and test data sets are sourced from the variable data set, whereas the OOD data set is obtained from heartbeat stars. The distribution is truncated for ODIN scores above 0.2.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: ODIN ROC curve with only PS component enabled, represented by the orange line, that is generated by varying the ODIN score threshold. The x-axis denotes the FPR, and the y-axis denotes the TPR. The diagonal dashed blue line illustrates the performance of a random detector. The dotted black lines indicate the 95% TPR and corresponding FPR.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results of ASAS-SN J082703.44-265001.6, which is a δ Sct object misclassified by our model. The 95% conformal predictive set (see Section 4.3) is [RRL/Cepheid, δ Sct/β Cep, Rot/CEB, EB/transit]. Similar to Figure 9.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Reliability diagram of our sample. The x-axis represents the confidence of the model, while the y-axis represents the accuracy of the model. The red step line shows the confidence and corresponding accuracy. The diagonal dashed line represents the ideal calibration, where the confidence is equal to the accuracy. The deviation from the diagonal line indicates the miscalibration of the model.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results of ASASSN-V J202436.43+133428.1, which is an EB system correctly classified in our model. Similar to Figure 9.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: UMAP projections of the penultimate feature layers of the LC and PS components. The left panel displays the UMAP of the LC component, while the right panel shows the UMAP of the PS component. The different colors represent different classes of objects.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results of ZTF 18abuhyjv, which is an SN Ia from the ZTF alert system. Similar to Figure 9 but plotted as gray scale for data from different observational bands. The gray points indicate the g band.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results for ZTF 19aapkqyk, an EB identified by the ZTF alert system. Similar to Figure A1; black points represent the r band, while gray points denote the g band.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results of KIC 10651945, which is an EB. Similar to Figure 9.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Intermediate classification results of KIC 10091792, which is a γ Dor. Similar to Figure 9.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Examples of two-component SED-fitting with stellar model magnitudes (Kurucz for the cooler companion, Tlusty for the hot companion). The Simbad identification, and spectral type (in parentheses), are given when available. Resulting T eff values are printed for both components, and of E  B−V  (derived for the cool star, assumed = 0.05 mag for the hot star). Radii derived from scaling the best-fit model are given first for a reference distance of 1 kpc, then rescaled for the distance derived from the Gaia parallax. Dots are observed magnitudes, the black thick line connects the best-fit model magnitudes, and the thin black lines show the acceptable range of solutions. The dashed line is the composite model SED from both components.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Left: model SEDs in GALEX+SDSS filters, covering a range of stellar pairs targeted by our study. Model magnitudes for cool stars (main sequence (MS) case shown) in black, WD models color coded (the stellar parameters of the purple model are appropriate for a central star of planetary nebula). Right: black dots are the single-star MS models as in the left panel, colored lines are composite WD+MS model magnitudes for sample pairs. At the ∼4.″5 and 1.″4 resolution of GALEX and SDSS imaging, all pairs are unresolved, and the SED looks like the dashed or dotted model-mag lines. The Figure illustrates the power of FUV−optical catalogs to identify candidate binaries in a regime different from what is accessible from optical data alone. Model magnitudes are scaled to a distance of 1 kpc, and to the stellar radii given in the legend.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Examples of SED-fitting for single hot-star candidates with derived T eff between 30 kK and 20 kK, from both Tlusty high gravity stellar models (log g range 6−8) and Kurucz stellar models (log g range 3−5). Resulting values, printed on the legend for both solutions, are generally consistent; the two best-fit model magnitude lines, both plotted with a black line, are indistinguishable on this scale. Most sources have a small radius, compatible with a compact evolved object; the solution from Kurucz model magnitudes finds the highest gravity available in the grid (log g = 5) as the best-fit case, but the actual gravity is likely higher in these examples, which is reflected in the discrepancy among the derived radii.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Distances derived from Gaia DR3 parallax for the binary- and single-star candidates that have a Gaia match and a parallax measurement with error ≤ 20%, shown with a thin and thick line, respectively. Actual values in the sample stretch to ∼6 kpc, with very few data points beyond the range shown.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Examples of SED fitting with Tlusty high gravity stellar models for single-star candidates with derived high T effs. The Simbad identification, and spectral type in parentheses if available, are given. The values of T eff and E  B−V  (derived as free parameters, and the best-fit log g) are printed; the radius, derived from the best-fit model scaling, is given for a reference distance of 1 kpc as well as rescaled for the distance derived from the Gaia parallax. Symbols are the same as in Figure 9.
Paper: High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam: Project Overview and First Data Release
Authors: Ming-Yang Zhuang, 明阳 庄, Qian Yang, Yue Shen, Monika Adamów, Douglas N. Friedel, R. A. Gruendl, Zachary Stone, Junyao Li, Xin Liu, Paul Martini, Timothy M. C. Abbott, Scott F. Anderson, Roberto J. Assef, Franz E. Bauer, Richard Bielby, W. N. Brandt, Colin J. Burke, Jorge Casares, Yu-Ching Chen, Gisella De Rosa, Alex Drlica-Wagner, Tom Dwelly, Alice Eltvedt, Gloria Fonseca Alvarez, Jianyang Fu, 健洋 傅, Cesar Fuentes, Melissa L. Graham, Catherine J. Grier, Nathan Golovich, Patrick B. Hall, Patrick Hartigan, Keith Horne, Anton M. Koekemoer, Mirko Krumpe, Jennifer I. Li, Chris Lidman, Umang Malik, Amelia Mangian, Andrea Merloni, Claudio Ricci, Mara Salvato, Rob Sharp, David E. Trilling, Brad E. Tucker, Di Wen, Zachary Wideman, Yongquan Xue, Zhefu Yu, Catherine Zucker
Figure caption: Histograms of objects used to calibrate the magnitude zero-point (zp) for each CCD image of every exposure. The histogram for point sources using m PSF is in red (PSF), while the histogram for all sources using m AUTO is in black.
Paper: High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam: Project Overview and First Data Release
Authors: Ming-Yang Zhuang, 明阳 庄, Qian Yang, Yue Shen, Monika Adamów, Douglas N. Friedel, R. A. Gruendl, Zachary Stone, Junyao Li, Xin Liu, Paul Martini, Timothy M. C. Abbott, Scott F. Anderson, Roberto J. Assef, Franz E. Bauer, Richard Bielby, W. N. Brandt, Colin J. Burke, Jorge Casares, Yu-Ching Chen, Gisella De Rosa, Alex Drlica-Wagner, Tom Dwelly, Alice Eltvedt, Gloria Fonseca Alvarez, Jianyang Fu, 健洋 傅, Cesar Fuentes, Melissa L. Graham, Catherine J. Grier, Nathan Golovich, Patrick B. Hall, Patrick Hartigan, Keith Horne, Anton M. Koekemoer, Mirko Krumpe, Jennifer I. Li, Chris Lidman, Umang Malik, Amelia Mangian, Andrea Merloni, Claudio Ricci, Mara Salvato, Rob Sharp, David E. Trilling, Brad E. Tucker, Di Wen, Zachary Wideman, Yongquan Xue, Zhefu Yu, Catherine Zucker
Figure caption: The layout of HELM pointings in (a) CDF-S, (b) COSMOS, (c) ELAIS-S1, (d) S-CVZ, (e) SDSS Stripe 82, and (f) XMM-LSS fields. Pointings 1, 2, and 3 are indicated in black, blue, and red colors, respectively.
Paper: High-quality Extragalactic Legacy-field Monitoring (HELM) with DECam: Project Overview and First Data Release
Authors: Ming-Yang Zhuang, 明阳 庄, Qian Yang, Yue Shen, Monika Adamów, Douglas N. Friedel, R. A. Gruendl, Zachary Stone, Junyao Li, Xin Liu, Paul Martini, Timothy M. C. Abbott, Scott F. Anderson, Roberto J. Assef, Franz E. Bauer, Richard Bielby, W. N. Brandt, Colin J. Burke, Jorge Casares, Yu-Ching Chen, Gisella De Rosa, Alex Drlica-Wagner, Tom Dwelly, Alice Eltvedt, Gloria Fonseca Alvarez, Jianyang Fu, 健洋 傅, Cesar Fuentes, Melissa L. Graham, Catherine J. Grier, Nathan Golovich, Patrick B. Hall, Patrick Hartigan, Keith Horne, Anton M. Koekemoer, Mirko Krumpe, Jennifer I. Li, Chris Lidman, Umang Malik, Amelia Mangian, Andrea Merloni, Claudio Ricci, Mara Salvato, Rob Sharp, David E. Trilling, Brad E. Tucker, Di Wen, Zachary Wideman, Yongquan Xue, Zhefu Yu, Catherine Zucker
Figure caption: Cadence of the HELM in different fields, with vertical red lines indicating individual exposures. Moon illumination (illuminated fraction of the moon) is indicated by the blue gradient (white = no moonlight; dark blue = maximum moonlight) in the background.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption:  q crit for 1.6 M ⊙ helium stars from He-ZAMS to C ignition. The results keep increasing on He-MS and He-HG. Due to the development of the convective envelope, q crit first decreases at the beginning of He-GB and then increases at later He-GB because of the radius increasing along with evolution. The “Failed data” area is for the models that we cannot appropriately simulate due to the steep entropy profile at the superadiabatic zone near the stellar surface. If the initial mass ratio of helium binary star is larger than q crit, the system shall go through unstable mass transfer stage. The red dashed–dotted line is the maximum mass ratio of Darwin instability (DWI), discussed in Section 4. If the mass ratio of a helium binary star is larger than DWI, the system shall be tidally unstable.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: The relation of 1/q crit and stellar binary orbital period. The color bar represents the stellar mass. We light the color on the expansion stage of He-MS and He-HG. The dotted line is a critical boundary for the network of progenitors of SN Ia when M accretor = 1.2M ☉ by Wang et al. (2009). Horizontal lines are the criteria of the polytropic models on He-MS and He-HG by Claeys et al. (2014). The data K24 of ϕ Per-type binaries is from Klement et al. (2024). We also put two indirect helium binaries, NGC 247 ULX-1, and V445 Pup, in this figure.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: Radius evolution tracks of two 1.6 M ⊙ helium stars during adiabatic mass loss. In panel (a), we show the total radiative structure of a He-TAMS star. In panel (b), it is a convective envelope on the giant branch. The black solid line is the radius evolutionary change. The red dashed line is the initial radius profile. Because of adiabatic expansion, we can see that the stellar surface radius is always larger than the initial profile. Using the method as the trigger of unstable mass transfer in Paper I (Ge et al. 2010), we show the Roche-lobe radius of Kelvin–Helmholtz mass transfer rate (R KH) as a gray dashed line. Assuming binary mass transfer is conservative and the mass ratio is known, the R L can be rebuilt as a blue dashed line during mass transfer. In this paper, M and R represent the total mass and surface radius. In contrast, m and r represent the mass and radius coordinate inside the star.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: In panel (a), we show the initial entropy profiles of 1.6 M ⊙ helium stars in different evolution stages. Panel (b) shows radius changes during adiabatic mass loss.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: Radius evolution of 1.6 M ⊙ helium star from He-ZAMS to C ignition. We use different colors to show the different trends of radius variation (the three phases introduced in Figure 1(b)). Due to the different evolution timescales and radius change magnitudes of He-MS and He-HG/GB, we use two panels to show the radius evolution in this figure. The left x-axis and y-axis are for the He-MS stage, while the right axes are for He-HG/GB. Dotted lines represent the maximum radius of He-MS. The shadow area is the convective envelope zone. The models we choose for adiabatic mass loss are black dots.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: The final status of helium stars. We roughly separate them by the carbon luminosity L C and center degeneracy Ψc. For M He < 1.4 M ⊙ stars, they will become CO WDs. For 1.4 M ⊙ < M He < 2.3 M ⊙ stars, off-center carbon flash reserves the degenerate core, and they end as ONe WDs. For M He > 2.3 M ⊙ helium stars, carbon ignition starts in the center, and they lead to CCSNe. Due to the helium envelope, the hydrogen lines do not exist in the spectrum (SN Ib). The special situations, CO-Ne WD and ECSN, cannot be certain in our simulation. We roughly give their top limits in the figure.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: Fitting q crit for helium mass sequences in M − R parameter space. This result uses RBF interpolating to fit the initial result of q crit. The shadow area represents failed data introduced in Section 3.1. To highlight the large results on He-HG/GB and follow the observation tradition, we use 1/q crit instead to show this parameter space.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: Initial results of stability criteria for helium mass sequences in mass–radius (M − R) parameter space. We use 1/q crit to highlight the lower results that q crit < 10. Here, we ignore the shrinkage at the later MS stage, and just consider the expanding phase of evolution. We show them in three parts: the early He-MS stage (Case BA) in panel (a), the He-HG stage (Case BBe) in panel (b), and the He-GB stage (Case BBl) in panel (c). The shadow area represents failed data introduced in Section 3.1.
Paper: Adiabatic Mass Loss in Binary Stars. IV. Low- and Intermediate-mass Helium Binary Stars
Authors: Lifu Zhang, Hongwei Ge, Xuefei Chen, Zhanwen Han
Figure caption: Mass sequences of helium stars in the HRD. We divided them into three parts out of their envelope feature. Blue lines: carbon ignition before developing convective envelope. Green lines: could evolve convective envelope. Red lines: envelope exhausted before developing convective envelope. The models that start carbon ignition are shown as black stars.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: Number per bin (top panels), differential (middle panels), and cumulative (bottom panels) distributions of waiting time for pulses in XRBs from SGR J1550-5418 (left panels) and SGR J1935+2154 (right panels). The occurrence rate is defined as the number divided by the width of this time-bin size. The red lines are the results obtained by fitting with Equation (1) (middle panels) and Equation (2) (bottom panels), respectively. The black dotted lines represent x 0 due to the threshold effect.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: The distributions of the number of pulses for SGR J1935+2154 and SGR J1550-5418. The red line is obtained by fitting the histogram with a single exponential function; the fitting result is ﹩y={1044.27}_{-86.73}^{+86.73}\times ({e}^{-x/{0.98}_{-0.04}^{+0.04}})﹩ with reduced-χ 2 = 0.64 for SGR J1935+2154, and ﹩y={394.17}_{-48.65}^{+48.65}\times ({e}^{-x/{1.21}_{-0.09}^{+0.09}})﹩ with reduced-χ 2 = 1.34 for SGR J1550-5418.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: Similar to Figure 3, but for the peak count rate of the pulses.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: Examples of pulses identified in two XRBs from SGR J1935+2154 observed by the NaI detectors with less than 60° on Fermi-GBM. The waiting time for pulses is the time difference between two neighboring peaks. The time-bin size of both light curves is 6.2 ms, which is the optimal time-bin size used in the pulse-finding algorithm, and the dotted lines represent the peaks of the pulses. The red lines are the estimated background by the BB algorithm.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: The total number of pulses identified at different time-bin sizes (the left panel for SGR J1550-5418 and the right panel for SGR J1935+2154); the red dotted lines are the optimal time-bin sizes (both are 6.2 ms) corresponding to the most identified pulses.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: Similar to Figure 3, but for the duration of pulses.
Paper: The Self-organized Criticality Behaviors of Pulses in Magnetar Bursts
Authors: Shuo Xiao, Mei-Xin Hong, Zi-Yi You, Tong-Lei Liao, Shao-Lin Xiong, Shu-Xu Yi, Ping Wang, Jiao-Jiao Yang, Zheng-Huo Jiang, Yue Wang, Yan-Qiu Zhang, Ming-Yu Ge, Zhen Zhang, Xi-Hong Luo, Shi-Jun Dang, Ru-shuang Zhao, Ai-Jun Dong, Qi-Jun Zhi, He Gao, Di Li, Shuang-Nan Zhang
Figure caption: Similar to Figure 3, but for the total pulse counts.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: SOM 2D representation of the training data set. Random jitter within each pixel has been added for clarity. Each colored point represents an LC in our training data set; the different colors correspond to different classes as denoted in the legend.