adsabs/AstroCLIMB huggingface dataset:
Paper: The Chemical Yields of Stars in the Mass Range 9–15 M⊙
Authors: Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
Figure caption: Selected element abundance ratios in the ejecta of stars with initial mass in the range 9.22−15 M⊙.
Paper: The Chemical Yields of Stars in the Mass Range 9–15 M⊙
Authors: Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
Figure caption: Density profiles of all the models at the presupernova stage. The filled dots and the filled diamonds mark the CO core and the He core, respectively.
Paper: The Chemical Yields of Stars in the Mass Range 9–15 M⊙
Authors: Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
Figure caption: He core mass (blue line/blue dots) and CO core mass (red line/red dots) as a function of the initial mass at the presupernova stage.
Paper: The Chemical Yields of Stars in the Mass Range 9–15 M⊙
Authors: Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
Figure caption: Chemical composition of all the models at the presupernova stage.
Paper: The Chemical Yields of Stars in the Mass Range 9–15 M⊙
Authors: Marco Limongi, Lorenzo Roberti, Agnese Falla, Alessandro Chieffi, Ken'ichi Nomoto
Figure caption: Same as Figure 7 but for the 12 M⊙ star.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Upper left: tensor transfer functions for five illustrative tensor modes. The two leftmost modes reentered the horizon during reheating (w = 0) and the two rightmost modes during the RD era (w = 1/3). The middle one reentered the horizon during kination (w = 1), and the vertical shade indicates its interval of integration. Upper right: stiff-amplified inflationary SGWB spectrum today. As explained in the text, the sampled frequencies, {fi}, are adaptively chosen such that the sampling is denser around transitions in ΩGW(f). The gray violins indicate the free spectrum obtained from the NANOGrav 15 yr data set (“NG15;” G. Agazie et al. 2023). The vertical dashed–dotted lines denote the same illustrative frequencies as in the upper left panel. The inset shows the blue-tilted primordial tensor spectra, where the nonlinearity cutoff is illustrated (see Appendix A.2), and the vertical shade denotes the PTA frequency band. The vertical dotted lines in both this panel and the inset denote the CMB scale, fCMB. Lower left: evolution of the effective number of extra relativistic species due to the stiff-amplified inflationary SGWB, ΔNeff,GW. Lower right: expansion history described by the evolution of the σ variable, similar to Figure 1. The vertical shades indicate the integration time spans of the five illustrative modes.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Example evolution of the σ variable, or equivalently, the EoS parameter. The time variable is given by the number of e-folds, N, a measure of the exponential expansion of the Universe. The evolution of σ(N) signifies the expansion history. The horizontal dashed lines mark the EoS parameters of the major cosmological phases: w = 0 during the matter-dominated era and reheating, w = 1/3 during the RD era, and w = 1 during kination. The features in the σ curve during the RD era indicate the phase transitions in the thermal history of the SM sector; see Appendix A.1. Key cosmological transitions are marked by the vertical dotted lines: ﹩{N}_{{\rm{re}}}﹩ denotes the end of reheating when all the inflationary energy is transformed to the energy of the stiff matter and the thermal bath; Nkr denotes the kination-radiation equality when the energy density of the stiff matter (ρs ∝ a−6) equals that of radiation (ρr ∝ a−4); Neq denotes the ordinary radiation-matter equality.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Sampled {fi, ΩGW(fi)} curves. This figure displays six representative ΩGW(fi) curves, illustrating three distinct morphological types defined by their monotonicity and frequency characteristics. The first type exhibits a rapid monotonic increase followed by an abrupt cutoff. The second type shows a monotonic decrease with a variable inflection point, where the rate of decrease shifts noticeably. The third, most complex type features multiple monotonicity transitions—initially decreasing, then increasing, and finally decreasing again—posing significant challenges for prediction due to variability in monotonicity, frequency range, and ΩGW(fi) amplitude. Different colors distinguish these types: red for monotonically increasing curves, yellow for monotonically decreasing (or nearly monotonic) curves, and blue for curves without monotonicity.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Frequency ranges of {fi}, or ﹩[{f}_{{\rm{\min }}},{f}_{{\rm{\max }}}]﹩, of all the samples in our data set. They vary inherently across the samples, and their distributions are statistically analyzed here. The horizontal axis represents sample indices, and the vertical axis indicates the frequency boundaries derived from each set of physical parameters. We find that ﹩{f}_{{\rm{\min }}}﹩ remains stable near 10−18.6 Hz, forming an approximately horizontal line, while ﹩{f}_{{\rm{\max }}}﹩ exceeds 10−15 Hz for all samples, distinguishable from the former.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Projected distribution of all samples in the data set after applying principal component analysis (PCA). We perform a dense search of the parameter space of ﹩\{r,{n}_{{\rm{t}}},{\kappa }_{10},{T}_{{\rm{re}}},{\rm{\Delta }}{N}_{{\rm{re}}}\}﹩ using a data set of 25,689 samples generated by LHS with their priors specified in Table 1. The resultant distribution after PCA processing is visualized by projecting the five-dimensional space onto a two-dimensional plane defined by the first two principal components.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Histogram of the ﹩{f}_{{\rm{\max }}}﹩ values of all samples in the data set. Same as in Figure 4, the orange and red regions represent the samples that cover the PTA frequency range and the LIGO frequency range, respectively.
Paper: SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation
Authors: Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro
Figure caption: Distribution of the number of sampled points/frequencies in all GW spectral curves in the data set, {ΩGW(fi)}, generated by stiffGWpy.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: Lightcurve of TIC 139079180 for Sector 15 (top panel) vs. the same lightcurve scaled with a quantile transform (bottom panel). Eclipses are highlighted in blue. The scattered light near the ends of the two segments dominates the lightcurve and also resembles an eclipse, making this type of feature difficult to overcome as a source of false positives. The quantile transform has the effect of making these events less prominent and emphasizing the actual eclipses. The inputs to the neural network are the quantile-scaled lightcurves. Although making classification perhaps more difficult to the human eye, the quantile transform represents the lightcurve to the neural network in a manner that allows it to successfully find and classify the eclipse feature.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: (Left panel) Scatter plot of the morphology parameter vs. Tmag for the Kepler EBs found (blue) and not found (red) by our neural network. (Right panel) The same plot for the TESS EB catalog. For both panels, the top histogram shows the distribution over morphology, while the right histogram shows the distribution by Tmag. Our performance against the Kepler EBs shows a clear preference for the central morphology range as well as a decline as magnitude decreases. Note that the Tmag distribution for the TESS EBs is limited by their selection as 2 minute cadence targets, hence the apparent cutoff at Tmag ≈ 12.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: (Left panel) Scatter plot of the EB period vs. Tmag for the Kepler EBs found (blue) and not found (red) by our neural network. (Right panel) The same plot for the TESS EB catalog. For both panels, the top histogram shows the distribution over EB period, while the right histogram shows the distribution by Tmag. Again, our performance against the Kepler EBs shows a decline as magnitude decreases. As with Figure 4, note that the Tmag distribution for the TESS EBs is limited by their selection as 2 minute cadence targets, hence the apparent cutoff at Tmag ≈ 12.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: As compared to the Kepler EB catalog, true positives (blue), false negatives (red), and false positives (green) identified by our neural network, shown here in the Kepler field. Local groupings of false positives are attributable to very bright EBs contaminating the lightcurves of adjacent stars or systematics such as scattered light, which closely resembled the shape of an eclipse in the overlapping TESS Sector 14.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: (Left panel) Scatter plot of the EB period vs. the morphology parameter for the Kepler EBs found (blue) and not found (red) by our neural network. (Right panel) The same plot for the TESS EB catalog. For both panels, the top histogram shows the distribution over EB period, while the right histogram shows the distribution by morphology parameter. Besides revealing the relationship between EB period and morphology as described by A. Prša et al. (2011), we can see a pattern of weakness of our neural network for EBs at the extremes of the period range for any given morphology range.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: The structure of our neural network, with like layers grouped by color. The full network summary is shown on the left. The attention block has a structure such as shown on the upper right, while each convolutional block has a structure such as shown on the lower right. Arrows into the addition layers indicate the flow of the residual.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: Neural network saliency map (shades of red according to the activation magnitude) for a segment of the TIC 214716930 lightcurve in TESS Sector 12 (blue), demonstrating the activation of the neural network in the penultimate convolutional layer on the feature of the eclipse, made using KERAS-VIS (R. Kotikalapudi 2017).
Paper: Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets
Authors: Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Fu-Xing Li, Qi-bin Sun, Wen-Ping Liao, Ping Li, Xiang-Dong Shi, Ting Li
Figure caption: Display of the asteroseismic parameters derived from analyzing the power spectrum of the exoplanet host star Kepler-432 using the pySYD package. More detailed explanations of each figure are available in the online documentation (https://pysyd.readthedocs.io/en/latest/library/output.html). The primary focus is on the middle-left and lower-left panels, which present the fits for ﹩{\nu }_{{\rm{\max }}}﹩ and Δν, respectively.
Paper: Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets
Authors: Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Fu-Xing Li, Qi-bin Sun, Wen-Ping Liao, Ping Li, Xiang-Dong Shi, Ting Li
Figure caption: A comparison between the stellar parameters derived from asteroseismology and those obtained through spectroscopic calculations is shown. In this figure, all spectroscopic parameters come from previously published literature. The three subplots, from left to right, depict comparisons of stellar mass, radius, and surface gravity, respectively. In each panel, the black dashed line is not a linear fit but rather a reference line indicating asteroseismic parameter equals the literature parameter.
Paper: Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets
Authors: Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Fu-Xing Li, Qi-bin Sun, Wen-Ping Liao, Ping Li, Xiang-Dong Shi, Ting Li
Figure caption: Left panel: an H-R diagram comparing exoplanet host stars with and without solar-like oscillations. The gray points represent the host stars of more than 5800 exoplanets plotted on the diagram, while the colored points with error bars correspond to 142 host stars exhibiting solar-like oscillations, color coded by stellar mass. Right panel: similar to the left panel, but with surface gravity on the vertical axis. Here, the colors of the solar-like oscillators reflect their stellar radii. Both panels only display a specific region of parameter space; other host stars fall outside this range and are not shown.
Paper: Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets
Authors: Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Fu-Xing Li, Qi-bin Sun, Wen-Ping Liao, Ping Li, Xiang-Dong Shi, Ting Li
Figure caption: Left panel: a local view of the TESS light curve for the giant host star HD 112640. Right panel: the power spectrum of HD 112640, plotted in dark blue, also exhibits significant solar-like oscillation features and displays characteristics of equally spaced frequency intervals. However, note that on the horizontal axis, the peak of the power excess is approximately 4.5 μHz, while Δν is about 0.3 μHz. Due to data limitations, the asteroseismic parameters for this star cannot be reliably estimated.
Paper: Asteroseismic Characterization of 142 Solar-like Oscillators Hosting Confirmed Exoplanets
Authors: Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Fu-Xing Li, Qi-bin Sun, Wen-Ping Liao, Ping Li, Xiang-Dong Shi, Ting Li
Figure caption: The global asteroseismic parameter relations of the host stars. The gray curve corresponds to parameter values from the SYD method (D. Huber et al. 2010, 2011): for ﹩{\nu }_{{\rm{\max }}}\lt 300\,\mu {\rm{Hz}}﹩, α = 0.267 and β = 0.760; for ﹩{\nu }_{{\rm{\max }}}\gt 300\,\mu {\rm{Hz}}﹩, α = 0.22 and β = 0.797.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: Four sectors of short-cadence TESS data for WASP-166 (TIC 408310006) showing the prominent transits of the known planet WASP-166 b. Two additional transit-like events can be seen in Sectors 62 and 89 (highlighted with red arrows), suggesting the potential presence of a second planet in the system.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: TESS lightcurve of the known EB TIC 234229841, where the primary and secondary eclipses “switch” places. In Sector 6, the deeper eclipses precede the heartbeat-like hump, the primary and secondary eclipses have similar depths in Sector 33, and in Sector 87 the deeper eclipses follow the hump.
Paper: The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists
Authors: Veselin B. Kostov, Brian P. Powell, Aline U. Fornear, Marco Z. Di Fraia, Robert Gagliano, Thomas L. Jacobs, Julien S. de Lambilly, Hugo A. Durantini Luca, Steven R. Majewski, Mark Omohundro, Jerome Orosz, Saul A. Rappaport, Ryan Salik, Donald Short, William Welsh, Svetoslav Alexandrov, Cledison Marcos da Silva, Erika Dunning, Gerd Gühne, Marc Huten, Michiharu Hyogo, Davide Iannone, Sam Lee, Christian Magliano, Manya Sharma, Allan Tarr, John Yablonsky, Sovan Acharya, Fred Adams, Thomas Barclay, Benjamin T. Montet, Susan Mullally, Greg Olmschenk, Andrej Prša, Elisa Quintana, Robert Wilson, Hasret Balcioglu, Ethan Kruse
Figure caption: Upper panel: TESS lightcurve of an EB with a pulsating component (TIC 22621932). Lower panel: TESS lightcurve of an EB exhibiting a pronounced heartbeat pattern (TIC 336538437; S. Solanki et al. 2025).