adsabs/AstroCLIMB huggingface dataset:
Paper: Signatures of Atmospheric Mass Loss and Planet Migration in the Time Evolution of Short-period Transiting Exoplanets
Authors: Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci, Kevin K. Hardegree-Ullman, Steven Giacalone, Jessie L. Christiansen, James G. Rogers, Akash Gupta, Rebekah I. Dawson, Tommi T. Koskinen, Kiersten M. Boley, Jason L. Curtis, Katia Cunha, Eric E. Mamajek, Sabina Sagynbayeva, Sakhee S. Bhure, David R. Ciardi, Preethi R. Karpoor, Kyle A. Pearson, Jon K. Zink, Gregory A. Feiden
Figure caption: Bar chart showing the distribution of targets in young clusters for TESS EM1, highlighting the distinction between clusters younger than 100 Myr and those older than 100 Myr. The bars represent the total number of members in the clusters (gray dashed lines), targets observed during TESS EM1 (orange), targets that have SPOC light curves available (purple), and FGK targets for which SPOC light curves are available (blue).
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: Comparison between our GPU-based source detection method and SExtractor in terms of astrometry and photometry information. (a) Shows the differences in magnitudes obtained using auto photometry from the two methods, with approximately 95% of the target photometry differences being less than 0.04. (b) Illustrates the differences in source positional information between the two methods, with around 98% of the target astrometry differences being less than 0.04 pixel.
Paper: Image Preprocessing Framework for Time-domain Astronomy in the Artificial Intelligence Era
Authors: Liang Cao, Peng Jia, Jiaxin Li, Yu Song, Chengkun Hou, Yushan Li
Figure caption: The histogram of SSIM between difference images obtained by our GPU-based image subtraction method and the HOTPANTS. As shown in this figure, our method could obtain similar results to those obtained by HOTPANTS, with times faster.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Dependence of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mass ratio. The solid gray line in the left panel represents the normalized histogram of the mock sample mass ratio. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship between the true number of binaries Nb and the inferred number of binaries ﹩{N}_{{\rm{b}}}^{{\rm{d}}}﹩ is examined in the mock sample. In the left column, the inferred number of binaries corresponds to the count obtained from our trained MLP model. The middle panel employs the exact same mass ratio distribution as the test sample to estimate the number of binaries, as described in Section 3.1.2. In the right column, in the absence of mass ratio distribution information, we assume a uniform mass ratio distribution to infer the number of binaries using the method outlined in Section 3.1.2. The symbols—open circle (red), open square (blue), filled triangle (purple), and open star (green)—represent uniform, normal, exponential, and negative exponential mass ratio distributions of the test samples, respectively.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mg for the mock sample. The solid gray line in the left panel represents the normalized histogram of the mock sample mg. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Confusion matrix: bb represents the number of binaries detected as binaries in our MLP models; sb represents the number of single stars detected as binaries in our MLP models; and bs represents the number of binaries detected as single stars in our MLP models. ss represents the number of single stars detected as single stars in our MLP models. This figure is consistent with Figure 3 in Paper I, and it is included here to provide a clearer explanation of the methodology.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Histogram of the apparent magnitude distribution for the mock samples. Different colors correspond to the seven bands (NUV, u, g, r, i, z, y) of CSST.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Histogram of the apparent magnitude distribution for the observed samples. Different colors correspond to the seven bands (NUV, u, g, r, i, z, y).
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship between the S/N of CSST and the apparent magnitudes across seven bands as denoted in the plot.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mg for mock sample without the NUV band. The solid gray line in the left panel represents the normalized histogram of the mock sample mg. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Dependence of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mass ratio. The solid cyan line in the left panel represents the normalized histogram of the observed sample’s mass ratio with the NUV band. The solid gray line in the left panel represents the normalized histogram of the expanded observed sample’s mass ratio with the NUV band. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship between the true number of binaries Nb and the inferred number of binaries ﹩{N}_{{\rm{b}}}^{{\rm{d}}}﹩ is examined in the observed sample with the NUV band. In the left column, the inferred number of binaries corresponds to the count obtained from our trained MLP model. The middle panel employs the exact same mass ratio distribution as the test sample to estimate the number of binaries, as described in Section 3.1.2. In the right column, in the absence of mass ratio distribution information, we assume a uniform mass ratio distribution to infer the number of binaries using the method outlined in Section 3.1.2. The symbols—open circle (red), open square (blue), open triangle (purple), and open star (green)—represent uniform, normal, exponential, and negative exponential mass ratio distributions of the test samples, respectively.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship between the true number of binaries Nb and the inferred number of binaries ﹩{N}_{{\rm{b}}}^{{\rm{d}}}﹩ is examined in the observed sample without the NUV band. In the left column, the inferred number of binaries corresponds to the count obtained from our trained MLP model. The middle panel employs the exact same mass ratio distribution as the test sample to estimate the number of binaries, as described in Section 3.1.2. In the right column, in the absence of mass ratio distribution information, we assume a uniform mass ratio distribution to infer the number of binaries using the method outlined in Section 3.1.2. The symbols—open circle (red), open square (blue), open triangle (purple), and filled star (green)—represent uniform, normal, exponential, and negative exponential mass ratio distributions of the test samples, respectively.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The relationship of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mg for observed sample without the NUV band. The solid gray line in the left panel represents the normalized histogram of the mock sample mg. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: Dependence of F1 (red solid line), ρb (blue dotted line), and ρs (black dashed line) on the mg for observed sample. The solid gray line in the left panel represents the normalized histogram of the observed sample mg. The gray dashed vertical lines indicate the boundaries of the bin widths we sampled. In the right panel, the points represent the mean from 1000 resampling tests for each data point, and the error bars denote the corresponding standard error. The error bars in different colors correspond to different variables: red represents F1, blue represents ρb, and black represents ρs.
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The mass ratio distribution of the observed sample with the NUV band (cyan), the mass ratio distribution of the expanded observed sample with the NUV band (black) and the four empirical mass ratio distributions used as hypothesized inputs: uniform (red), normal (blue), exponential (purple), and reverse exponential (green).
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The mass ratio distribution of the mock sample (black) and the four empirical mass ratio distributions used as hypothesized inputs: uniform (red), normal (blue), exponential (purple), and reverse exponential (green).
Paper: Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX
Authors: Jia-jia Li, Jian-ping Xiong, Zhi-jia Tian, Chao Liu, Zhan-wen Han, Xue-fei Chen
Figure caption: The mass ratio distribution of the observed sample without the NUV band (black) and the four empirical mass ratio distributions used as hypothesized inputs: uniform (red), normal (blue), exponential (purple), and reverse exponential (green).
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Histograms of the distances for the stars in our sample. Sources in blue are part of the Breakthrough Listen nearby star sample compiled by H. Isaacson et al. (2017), while sources in red are all stars observed. Distances to galaxies and globular clusters in our sample are not shown.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Distribution of sources in our sample in equatorial coordinates. Sources with data available for all four GBT receivers used are plotted in blue, sources observed in three bands are in yellow, sources observed in two bands are in green, and sources observed in only one band are in red.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: The probability that a signal is present in at least two ON scans within a frequency block as a function of drift rate. A frequency block of at least 1024 frequency bins (2.86 kHz) is needed to guarantee that a signal with a drift rate of ±4 Hz s−1 falls within the bounds of at least two of the ON scans.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: The distribution (blue histogram) of hotspot regions (2.86 kHz frequency blocks from individual observations) across the L band, with kurtoses above our threshold but before any further filtering is performed. Red rectangles highlight heavy RFI regions, and contain 97% of the frequency blocks.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Kurtosis as a function of signal strength for a narrowband signal present in each time bin of an observation. Only frequency blocks of size 512 and 1024 have an expected kurtosis >​​​​​​1 for a signal with SNR = 10. This is true for the L, S, C, and X bands.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: An example signal that would be rejected by our broadband filter. The left panels contain the waterfall plot of the signal, with frequency on the x-axis and time on the y-axis, while the right panels show the frequency-integrated observations, with normalized intensity on the x-axis and time on the y-axis. The dashed red line is 10σ above the median, where σ is the median absolute deviation. The red star highlights time bins identified by a peak-finding algorithm above this level. The white dashed lines on the left panel show the extent of the 1024-channel block that our pipeline analyzes.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Scatterplot of all blocks analyzed by our pipeline according to their median ON scan kurtosis and maximum OFF scan kurtosis. Level 0 blocks are shown in black, Level 1 blocks in blue, and Level 2 blocks in orange. The dashed red lines represent the kurtosis cutoffs described in Section 3.4. All Level 1 and Level 2 blocks thus fall in the upper-left quadrant, which contains blocks with signals that appear strongly in the ON scans and weakly in the OFF scans. The vast majority of blocks fall along the ﹩{K}_{{\rm{OFF,max}}}={k}_{{\rm{ON,med}}}﹩ line, as most blocks contain RFI signals that appear equally strongly in all ON and OFF scans.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Common types of false positives that our pipeline deems Level 2 blocks. The drift rates in the heading of each figure are estimates produced by the pipeline, and may not reflect the true drift rate of the signal.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Fractional histograms of SNR for a subsample of all Level 0 blocks (blue) and the full sample of all Level 2 blocks (red) for each GBT receiver band.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: The frequency distribution of Level 1 and Level 2 blocks for each band. Red regions are frequencies with high-RFI densities that are excluded from our analysis. Gray regions represent the GBT notch filters and are also excluded.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Blocks qualified as Level 2 by our pipeline that do not contain singular, narrowband, linearly drifting signals.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Example Level 0 block.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: Six candidate signals, with their dedrifted, time-integrated spectra plotted.
Paper: A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive
Authors: Caleb Painter, Steve Croft, Matthew Lebofsky, Alex Andersson, Carmen Choza, Vishal Gajjar, Danny Price, Andrew P. V. Siemion
Figure caption: The frequency distribution of all Level 0 blocks.
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Initial stellar mass estimation of ASCC 19 and ASCC 21 after applying the membership probability cut. Left panel: the observed mass distribution of ASCC 19, with fitted IMFs of varied total mass. Right panel: the observed mass distribution of ASCC 21, with fitted IMFs of varied total mass. The parameters presented in this figure are based on the member data sourced from M. G. J. van Groeningen et al. (2023).
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: The 3D projected motion trajectories of ASCC 19 and ASCC 21 in the X–Y (left), Y–Z (middle), and X–Z (right) planes. The current positions of ASCC 19 and ASCC 21 are marked by a black filled pentagram and a black filled triangle, respectively. The positions after 40 Myr are denoted by a red filled pentagram (ASCC 19) and a blue filled triangle (ASCC 21). The time span between two neighboring symbol markers is 20 Myr for both red and blue trajectories. The parameters presented in this figure are calculated via the member data sourced from M. G. J. van Groeningen et al. (2023).
Paper: An In-depth Investigation of the Primordial Cluster Pair ASCC 19 and ASCC 21
Authors: Qingshun Hu, Yuting Li, Mingfeng Qin, Chenglong Lv, Yang Pan, Yangping Luo, Shuo Ma
Figure caption: Same as Figure 2, but all gray circles in each panel represent the cluster members with membership probability ranging from 0 to 1. All member data are sourced from M. G. J. van Groeningen et al. (2023).