adsabs/AstroCLIMB huggingface dataset:
Paper:  β Cephei Pulsators in Eclipsing Binaries Observed with TESS
Authors: Christian I. Eze, Gerald Handler
Figure caption: Binned phase diagrams of light curves of the ellipsoidal variables with β Cep pulsating component(s) after prewhitening the strongest pulsations.
Paper:  β Cephei Pulsators in Eclipsing Binaries Observed with TESS
Authors: Christian I. Eze, Gerald Handler
Figure caption: Fourier spectra of the likely ellipsoidal variables with β Cep components after removing the orbital light variations.
Paper:  β Cephei Pulsators in Eclipsing Binaries Observed with TESS
Authors: Christian I. Eze, Gerald Handler
Figure caption: Fourier spectra of the candidates for β Cep pulsators in eclipsing binaries after removing the orbital light variations.
Paper:  β Cephei Pulsators in Eclipsing Binaries Observed with TESS
Authors: Christian I. Eze, Gerald Handler
Figure caption: Fourier spectra of the definite β Cep pulsators in eclipsing binaries after removing the orbital light variations.
Paper: The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results
Authors: Yue Shen, Catherine J. Grier, Keith Horne, Zachary Stone, Jennifer I. Li, Qian Yang, Yasaman Homayouni, Jonathan R. Trump, Scott F. Anderson, W. N. Brandt, Patrick B. Hall, Luis C. Ho, Linhua Jiang, Patrick Petitjean, Donald P. Schneider, Charling Tao, Fergus. R. Donnan, Yusra AlSayyad, Matthew A. Bershady, Michael R. Blanton, Dmitry Bizyaev, Kevin Bundy, Yuguang Chen, Megan C. Davis, Kyle Dawson, Xiaohui Fan, Jenny E. Greene, Hannes Gröller, Yucheng Guo, Héctor Ibarra-Medel, Yuanzhe Jiang, Ryan P. Keenan, Juna A. Kollmeier, Cassandra Lejoly, Zefeng Li, Axel de la Macorra, Maxwell Moe, Jundan Nie, Graziano Rossi, Paul S. Smith, Wei Leong Tee, Anne-Marie Weijmans, Jiachuan Xu, Minghao Yue, Xu Zhou, Zhimin Zhou, Hu Zou
Figure caption: Tests of lag detection with more stringent quality cuts. In each panel, the open symbols represent the fiducial lag sample, and the filled symbols are for the subset. The left column shows results with a ﹩{r}_{\max }\gt 0.6﹩ cut; the middle column shows results with an SNR2 > 35 cut; the right column shows the results with the manual inspection cut. Details regarding these quality cuts and their impact on the measured R–L relations are presented in Appendix C.
Paper: The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results
Authors: Yue Shen, Catherine J. Grier, Keith Horne, Zachary Stone, Jennifer I. Li, Qian Yang, Yasaman Homayouni, Jonathan R. Trump, Scott F. Anderson, W. N. Brandt, Patrick B. Hall, Luis C. Ho, Linhua Jiang, Patrick Petitjean, Donald P. Schneider, Charling Tao, Fergus. R. Donnan, Yusra AlSayyad, Matthew A. Bershady, Michael R. Blanton, Dmitry Bizyaev, Kevin Bundy, Yuguang Chen, Megan C. Davis, Kyle Dawson, Xiaohui Fan, Jenny E. Greene, Hannes Gröller, Yucheng Guo, Héctor Ibarra-Medel, Yuanzhe Jiang, Ryan P. Keenan, Juna A. Kollmeier, Cassandra Lejoly, Zefeng Li, Axel de la Macorra, Maxwell Moe, Jundan Nie, Graziano Rossi, Paul S. Smith, Wei Leong Tee, Anne-Marie Weijmans, Jiachuan Xu, Minghao Yue, Xu Zhou, Zhimin Zhou, Hu Zou
Figure caption: Additional examples of Hβ lags (light curves in the left panel, and ICCF in the right panel). Higher (visual inspection) grades correspond to better measured lags. The continuum and broad-line light curves are normalized to have unity mean within their respective baseline, and therefore, they do not necessarily line up after shifting the broad-line light curve by the observed-frame PyROA lag.
Paper: The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results
Authors: Yue Shen, Catherine J. Grier, Keith Horne, Zachary Stone, Jennifer I. Li, Qian Yang, Yasaman Homayouni, Jonathan R. Trump, Scott F. Anderson, W. N. Brandt, Patrick B. Hall, Luis C. Ho, Linhua Jiang, Patrick Petitjean, Donald P. Schneider, Charling Tao, Fergus. R. Donnan, Yusra AlSayyad, Matthew A. Bershady, Michael R. Blanton, Dmitry Bizyaev, Kevin Bundy, Yuguang Chen, Megan C. Davis, Kyle Dawson, Xiaohui Fan, Jenny E. Greene, Hannes Gröller, Yucheng Guo, Héctor Ibarra-Medel, Yuanzhe Jiang, Ryan P. Keenan, Juna A. Kollmeier, Cassandra Lejoly, Zefeng Li, Axel de la Macorra, Maxwell Moe, Jundan Nie, Graziano Rossi, Paul S. Smith, Wei Leong Tee, Anne-Marie Weijmans, Jiachuan Xu, Minghao Yue, Xu Zhou, Zhimin Zhou, Hu Zou
Figure caption: Additional examples of C IV lags. Higher (visual inspection) grades correspond to better measured lags.
Paper: The Sloan Digital Sky Survey Reverberation Mapping Project: Key Results
Authors: Yue Shen, Catherine J. Grier, Keith Horne, Zachary Stone, Jennifer I. Li, Qian Yang, Yasaman Homayouni, Jonathan R. Trump, Scott F. Anderson, W. N. Brandt, Patrick B. Hall, Luis C. Ho, Linhua Jiang, Patrick Petitjean, Donald P. Schneider, Charling Tao, Fergus. R. Donnan, Yusra AlSayyad, Matthew A. Bershady, Michael R. Blanton, Dmitry Bizyaev, Kevin Bundy, Yuguang Chen, Megan C. Davis, Kyle Dawson, Xiaohui Fan, Jenny E. Greene, Hannes Gröller, Yucheng Guo, Héctor Ibarra-Medel, Yuanzhe Jiang, Ryan P. Keenan, Juna A. Kollmeier, Cassandra Lejoly, Zefeng Li, Axel de la Macorra, Maxwell Moe, Jundan Nie, Graziano Rossi, Paul S. Smith, Wei Leong Tee, Anne-Marie Weijmans, Jiachuan Xu, Minghao Yue, Xu Zhou, Zhimin Zhou, Hu Zou
Figure caption: Additional examples of Mg II lags. Higher (visual inspection) grades correspond to better measured lags.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The process of our searching method, and the matching result. We compared our 1963 candidates with events that existed in Fermi catalogs and the works (a) Zou et al. (2021), (b) Xie et al. (2022), and (c) Rehan & Ibrahim (2023). The number in parentheses presents the published bursts in total. There are 1536 candidates left, shown as “Other,” which indicates the nonmatched events. These results correspond to Tables 4 and 6.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The 2D and 3D visualization of the count maps is achieved using the dimensionality reduction method t-SNE, with T 90 information embedded for each sample. The count maps are the samples in the test set without normalized preprocessing. The T 90 of each GRB sample is derived from the corresponding GRB in the Fermi burst catalog. It is not able to calculate the duration of the non-GRB samples. The distributions of primary count maps in feature space for the GRB and non-GRB categories are indistinguishable.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The 2D and 3D visualizations of the feature maps are achieved using the dimensionality reduction method t-SNE, with T 90 embedded for each sample. The feature maps are output by the last convolutional layer of the optimal model on the test set. The distributions of feature maps in feature space for the GRB and non-GRB categories are indistinguishable. The feature maps of GRB and non-GRB categories show clear aggregations. The durations of the GRBs of FN samples were relatively short.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The 2D and 3D visualizations of the feature maps are achieved using the dimensionality reduction method t-SNE, with standard deviation of the hardness ratio embedded for each sample. The S/Ns of the GRBs of FN samples were relatively low. The dispersion of GRB hardness ratios for FN samples is relatively large.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The ROC curves depicting the true-positive rate ﹩\left(\tfrac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}\right)﹩ and the false-positive rate ﹩\left(\tfrac{\mathrm{FP}}{\mathrm{FP}+\mathrm{TN}}\right)﹩.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The mapping curves of the four GRBs that our model failed to identify. The green background indicates that the model distinguished the burst signal. The time of the curve is relative to the trigger time of Fermi/GBM. The significance is derived from the TRIGDAT file of GBM burst data products.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The mapping curves of seven subthreshold GRBs (GCN circular) that our model identified. The asterisk indicates that the model distinguished the burst signal on only one detector. The time of the curve is relative to the trigger time in the GCN. The gray period is T 90, and its calculation method is shown in Appendix B.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The 2D and 3D visualizations of the feature maps are achieved using the dimensionality reduction method t-SNE, with hardness ratio embedded for each sample. There is no clear pattern found in the hardness ratio of the GRBs of FN samples.
Paper: Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data
Authors: Peng Zhang, 鹏 张, Bing Li, 兵 李, Renzhou Gui, 任舟 桂, Shaolin Xiong, 少林 熊, Ze-Cheng Zou, 泽城 邹, Xianggao Wang, 祥高 王, Xiaobo Li, 小波 李, Ce Cai, 策 蔡, Yi Zhao, 一 赵, Yanqiu Zhang, 艳秋 张, Wangchen Xue, 王陈 薛, Chao Zheng, 超 郑, Hongyu Zhao, 宏宇 赵
Figure caption: The 2D and 3D visualizations of the feature maps are achieved using the dimensionality reduction method t-SNE, with S/N (calculation see Appendix B) embedded for each sample. The S/Ns of the GRBs of FN samples were relatively low.
Paper: A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Authors: Peiyun Qiao, 佩云 乔, Tingting Xu, 婷婷 许, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Lei Tan, 磊 谈, Chao Liu, 超 刘
Figure caption: The histogram of predicted variable source types.
Paper: A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Authors: Peiyun Qiao, 佩云 乔, Tingting Xu, 婷婷 许, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Lei Tan, 磊 谈, Chao Liu, 超 刘
Figure caption: The feature importance of LightGBM and XGBoost.
Paper: A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Authors: Peiyun Qiao, 佩云 乔, Tingting Xu, 婷婷 许, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Lei Tan, 磊 谈, Chao Liu, 超 刘
Figure caption: The confusion matrices and ROC curves of LightGBM and XGBoost.
Paper: A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Authors: Peiyun Qiao, 佩云 乔, Tingting Xu, 婷婷 许, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Lei Tan, 磊 谈, Chao Liu, 超 刘
Figure caption: The histogram of the number of different variable source types in the initial sample set.
Paper: A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Authors: Peiyun Qiao, 佩云 乔, Tingting Xu, 婷婷 许, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Lei Tan, 磊 谈, Chao Liu, 超 刘
Figure caption: The data-flow diagram of the sample set preparation.
Paper: TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field
Authors: Rosalia O'Brien, Rolf A. Jansen, Norman A. Grogin, Seth H. Cohen, Brent M. Smith, Ross M. Silver, W. P. Maksym, Rogier A. Windhorst, Timothy Carleton, Anton M. Koekemoer, Nimish P. Hathi, Christopher N. A. Willmer, Brenda L. Frye, M. Alpaslan, M. L. N. Ashby, T. A. Ashcraft, S. Bonoli, W. Brisken, N. Cappelluti, F. Civano, C. J. Conselice, V. S. Dhillon, S. P. Driver, K. J. Duncan, R. Dupke, M. Elvis, G. G. Fazio, S. L. Finkelstein, H. B. Gim, A. Griffiths, H. B. Hammel, M. Hyun, M. Im, V. R. Jones, D. Kim, B. Ladjelate, R. L. Larson, S. Malhotra, M. A. Marshall, S. N. Milam, J. D. R. Pierel, J. E. Rhoads, S. A. Rodney, H. J. A. Röttgering, M. J. Rutkowski, R. E. Ryan, M. J. Ward, C. W. White, R. J. van Weeren, X. Zhao, J. Summers, J. C. J. D'Silva, R. Ortiz, A. S. G. Robotham, D. Coe, M. Nonino, N. Pirzkal, H. Yan, T. Acharya
Figure caption: Calibration of photometric uncertainties to identify variable sources. We plot the difference in measured F606W magnitude, Δm 0.24, vs. the average measured F606W magnitude, m 0.24, for objects observed more than once in regions of overlapping HST coverage, where Δm 0.24 is defined in the sense Visit 2 − Visit 1. Each small gray point is an individual object, as detected and measured with SExtractor. The large brown circles depict SExtractor photometric errors, scaled by a factor 1.15 to match the observed distribution of magnitude differences (shown as small black circles) measured in 0.5 mag wide bins for 25.5 < m 0.24 ≤ 28.0 mag such that ∼68.3% of all data points fall between them. The dashed black curves represent the inner ∼68.3% range of the distribution at each magnitude and are polynomial fits to the black points. The brown curves therefore represent median 1σ, 2σ, and 3σ ranges as fit to the brown circles. Blue squares identify objects that vary at ≥3σ Δm  in F606W, where σ Δm  is defined in Equation (1). The histogram on the right-hand side illustrates the distribution of magnitude differences for all objects detected in the field. In gray, we list mean, median, standard deviation, and median absolute deviation of the y-axis distribution.
Paper: TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field
Authors: Rosalia O'Brien, Rolf A. Jansen, Norman A. Grogin, Seth H. Cohen, Brent M. Smith, Ross M. Silver, W. P. Maksym, Rogier A. Windhorst, Timothy Carleton, Anton M. Koekemoer, Nimish P. Hathi, Christopher N. A. Willmer, Brenda L. Frye, M. Alpaslan, M. L. N. Ashby, T. A. Ashcraft, S. Bonoli, W. Brisken, N. Cappelluti, F. Civano, C. J. Conselice, V. S. Dhillon, S. P. Driver, K. J. Duncan, R. Dupke, M. Elvis, G. G. Fazio, S. L. Finkelstein, H. B. Gim, A. Griffiths, H. B. Hammel, M. Hyun, M. Im, V. R. Jones, D. Kim, B. Ladjelate, R. L. Larson, S. Malhotra, M. A. Marshall, S. N. Milam, J. D. R. Pierel, J. E. Rhoads, S. A. Rodney, H. J. A. Röttgering, M. J. Rutkowski, R. E. Ryan, M. J. Ward, C. W. White, R. J. van Weeren, X. Zhao, J. Summers, J. C. J. D'Silva, R. Ortiz, A. S. G. Robotham, D. Coe, M. Nonino, N. Pirzkal, H. Yan, T. Acharya
Figure caption: Comparison of galaxy properties in the general galaxy population and in galaxies that exhibit variability. Shown are distributions of (a) photometric redshifts (z ph), (b) stellar mass, (c) stellar population age, (d) sSFR, and (e) extinction by dust (A  V ), for the general population (solid gray) and galaxies with variability (blue hatched). The y-axis labels on the left-hand side of each plot represent the number of galaxies in the general population, and those on the right-hand side (in blue) represent the number of galaxies with variability. Above each histogram panel, we plot the fraction of galaxies with variability (100% × N var/N general) for each bin. The uncertainties assume Poisson statistics with ﹩{\sigma }_{N}\propto \sqrt{N}﹩. This comparison only includes the subset of galaxies already flagged as reliable in Section 3.2.1, with both HST and JWST imaging (where photometric redshifts and SEDs can be fit), with z ph < 5.5, χ 2 < 10, and where the 2σ confidence interval in p(z) spans <1 redshift unit.
Paper: TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field
Authors: Rosalia O'Brien, Rolf A. Jansen, Norman A. Grogin, Seth H. Cohen, Brent M. Smith, Ross M. Silver, W. P. Maksym, Rogier A. Windhorst, Timothy Carleton, Anton M. Koekemoer, Nimish P. Hathi, Christopher N. A. Willmer, Brenda L. Frye, M. Alpaslan, M. L. N. Ashby, T. A. Ashcraft, S. Bonoli, W. Brisken, N. Cappelluti, F. Civano, C. J. Conselice, V. S. Dhillon, S. P. Driver, K. J. Duncan, R. Dupke, M. Elvis, G. G. Fazio, S. L. Finkelstein, H. B. Gim, A. Griffiths, H. B. Hammel, M. Hyun, M. Im, V. R. Jones, D. Kim, B. Ladjelate, R. L. Larson, S. Malhotra, M. A. Marshall, S. N. Milam, J. D. R. Pierel, J. E. Rhoads, S. A. Rodney, H. J. A. Röttgering, M. J. Rutkowski, R. E. Ryan, M. J. Ward, C. W. White, R. J. van Weeren, X. Zhao, J. Summers, J. C. J. D'Silva, R. Ortiz, A. S. G. Robotham, D. Coe, M. Nonino, N. Pirzkal, H. Yan, T. Acharya
Figure caption: Histograms of the number of variable candidates (light gray) and estimated numbers of false positives (red) and genuine variable sources (blue) as a function of the significance (in units of σ Δm ) of the variability detected. The number of genuine variables and the number of false positives add up to the total number of candidates in each bin. The red labels above each bin show the percentage of all objects in the field that are expected to be false positives for that σ Δm  bin. The top panel is for sources that varied by more than 3σ Δm  in F606W, and the bottom panel is for sources that varied by more than 2σ Δm  in both F606W and F435W, and did so in the same sense. Hence, assuming Gaussian statistics, the top panel reflects a two-sided test, and the bottom panel reflects a one-sided test. In total, we estimate ∼80 false positives in the sample of sources that varied by more than 3σ Δm  in F606W, the sample of sources that varied by more than 3σ Δm  in F435W, and the sample of sources that varied by more than 2σ Δm  in both F606W and F435W.
Paper: TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field
Authors: Rosalia O'Brien, Rolf A. Jansen, Norman A. Grogin, Seth H. Cohen, Brent M. Smith, Ross M. Silver, W. P. Maksym, Rogier A. Windhorst, Timothy Carleton, Anton M. Koekemoer, Nimish P. Hathi, Christopher N. A. Willmer, Brenda L. Frye, M. Alpaslan, M. L. N. Ashby, T. A. Ashcraft, S. Bonoli, W. Brisken, N. Cappelluti, F. Civano, C. J. Conselice, V. S. Dhillon, S. P. Driver, K. J. Duncan, R. Dupke, M. Elvis, G. G. Fazio, S. L. Finkelstein, H. B. Gim, A. Griffiths, H. B. Hammel, M. Hyun, M. Im, V. R. Jones, D. Kim, B. Ladjelate, R. L. Larson, S. Malhotra, M. A. Marshall, S. N. Milam, J. D. R. Pierel, J. E. Rhoads, S. A. Rodney, H. J. A. Röttgering, M. J. Rutkowski, R. E. Ryan, M. J. Ward, C. W. White, R. J. van Weeren, X. Zhao, J. Summers, J. C. J. D'Silva, R. Ortiz, A. S. G. Robotham, D. Coe, M. Nonino, N. Pirzkal, H. Yan, T. Acharya
Figure caption: Color composites of 12 representative variable candidates, with F606W, F435W, and F275W shown in red, green, and blue hues, respectively. Each row of 100 × 100 pixel (3″ × 3″) cutouts corresponds to a different sample: (1) F606W-only 3σ Δm , (2) F435W-only 3σ Δm , (3) F435W and F606W 2σ Δm , and (4) F435W and F606W 3σ Δm  variability. A cyan or black circle at the center of each cutout represents the 0.″24 aperture in which variability was detected. Above each cutout, we note the visit identifier and decimal year of observation. At the top of each cutout, we include the measured m 0.24 magnitude for that visit. In the left panel of each pair of cutouts, at the bottom, we list the variable candidate identification number and whether it is variable in the F606W filter, the F435W filter, or both. Most of the variable candidates will be AGNs, although some will be faint or obscured SNe.
Paper: TREASUREHUNT: Transients and Variability Discovered with HST in the JWST North Ecliptic Pole Time-domain Field
Authors: Rosalia O'Brien, Rolf A. Jansen, Norman A. Grogin, Seth H. Cohen, Brent M. Smith, Ross M. Silver, W. P. Maksym, Rogier A. Windhorst, Timothy Carleton, Anton M. Koekemoer, Nimish P. Hathi, Christopher N. A. Willmer, Brenda L. Frye, M. Alpaslan, M. L. N. Ashby, T. A. Ashcraft, S. Bonoli, W. Brisken, N. Cappelluti, F. Civano, C. J. Conselice, V. S. Dhillon, S. P. Driver, K. J. Duncan, R. Dupke, M. Elvis, G. G. Fazio, S. L. Finkelstein, H. B. Gim, A. Griffiths, H. B. Hammel, M. Hyun, M. Im, V. R. Jones, D. Kim, B. Ladjelate, R. L. Larson, S. Malhotra, M. A. Marshall, S. N. Milam, J. D. R. Pierel, J. E. Rhoads, S. A. Rodney, H. J. A. Röttgering, M. J. Rutkowski, R. E. Ryan, M. J. Ward, C. W. White, R. J. van Weeren, X. Zhao, J. Summers, J. C. J. D'Silva, R. Ortiz, A. S. G. Robotham, D. Coe, M. Nonino, N. Pirzkal, H. Yan, T. Acharya
Figure caption: We analyze the potential effect of CRs on the measured flux difference of variable objects in an 8 × 8 pixel (0.″24 × 0.″24) area centered on each object. A pixel is considered at risk of CR contamination if it encounters a CR in four or more contributing exposures. Plotted is a random selection of 10,000 galaxies (represented by black points), with median values indicated by green boxes. Left: the absolute value of the difference in measured F606W flux vs. the total number of pixels (in Visit 1 + Visit 2) possibly impacted by CRs. The red shaded region shows objects that are removed for containing too many CRs. The y-axis is logarithmically scaled except between −0.1 and 0.1, which is scaled linearly. The x-axis is linearly scaled up to 20, where it is then logarithmically scaled to 50. Right: the absolute value of the difference in measured F606W flux vs. the difference in the number of pixels potentially affected by CRs.