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
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2025
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| author | Kostov, Veselin B. Powell, Brian P. Fornear, Aline U. Di Fraia, Marco Z. Gagliano, Robert Jacobs, Thomas L. de Lambilly, Julien S. Luca, Hugo A. Durantini Majewski, Steven R. Omohundro, Mark Orosz, Jerome Rappaport, Saul A. Salik, Ryan Short, Donald Welsh, William Alexandrov, Svetoslav da Silva, Cledison Marcos Dunning, Erika Guhne, Gerd Huten, Marc Hyogo, Michiharu Iannone, Davide Lee, Sam Magliano, Christian Sharma, Manya Tarr, Allan Yablonsky, John Acharya, Sovan Adams, Fred Barclay, Thomas Montet, Benjamin T. Mullally, Susan Olmschenk, Greg Prsa, Andrej Quintana, Elisa Wilson, Robert Balcioglu, Hasret Kruse, Ethan Collaboration, the Eclipsing Binary Patrol |
| author_facet | Kostov, Veselin B. Powell, Brian P. Fornear, Aline U. Di Fraia, Marco Z. Gagliano, Robert Jacobs, Thomas L. de Lambilly, Julien S. Luca, Hugo A. Durantini Majewski, Steven R. Omohundro, Mark Orosz, Jerome Rappaport, Saul A. Salik, Ryan Short, Donald Welsh, William Alexandrov, Svetoslav da Silva, Cledison Marcos Dunning, Erika Guhne, Gerd Huten, Marc Hyogo, Michiharu Iannone, Davide Lee, Sam Magliano, Christian Sharma, Manya Tarr, Allan Yablonsky, John Acharya, Sovan Adams, Fred Barclay, Thomas Montet, Benjamin T. Mullally, Susan Olmschenk, Greg Prsa, Andrej Quintana, Elisa Wilson, Robert Balcioglu, Hasret Kruse, Ethan Collaboration, the Eclipsing Binary Patrol |
| contents | The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05631 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Kostov, Veselin B. Powell, Brian P. Fornear, Aline U. Di Fraia, Marco Z. Gagliano, Robert Jacobs, Thomas L. de Lambilly, Julien S. Luca, Hugo A. Durantini Majewski, Steven R. Omohundro, Mark Orosz, Jerome Rappaport, Saul A. Salik, Ryan Short, Donald Welsh, William Alexandrov, Svetoslav da Silva, Cledison Marcos Dunning, Erika Guhne, Gerd Huten, Marc Hyogo, Michiharu Iannone, Davide Lee, Sam Magliano, Christian Sharma, Manya Tarr, Allan Yablonsky, John Acharya, Sovan Adams, Fred Barclay, Thomas Montet, Benjamin T. Mullally, Susan Olmschenk, Greg Prsa, Andrej Quintana, Elisa Wilson, Robert Balcioglu, Hasret Kruse, Ethan Collaboration, the Eclipsing Binary Patrol Solar and Stellar Astrophysics Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec). |
| title | 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 |
| topic | Solar and Stellar Astrophysics Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2506.05631 |