Using machine learning method for variable star classification using the TESS Sectors 1-57 data

• URL: http://arxiv.org/abs/2504.00347v1
• Date: Tue, 01 Apr 2025 01:58:23 GMT
• Title: Using machine learning method for variable star classification using the TESS Sectors 1-57 data
• Authors: Li-Heng Wang, Kai Li, Xiang Gao, Ya-Ni Guo, Guo-You Sun,
• Abstract summary: The Transiting Exoplanet Survey Satellite (TESS) is a wide-field all-sky survey mission designed to detect Earth-sized exoplanets.<n>Data from sectors 1-57, including approximately 1,050,000 light curves with a 2-minute cadence, were collected.<n>By cross-matching the data with Gaia's variable star catalogue, we obtained labeled datasets for further analysis.<n>We obtained 6046 EA, 3859 EW, 2058 CEP, 8434 DSCT, 482 RRab, 416 RRc, and 9694 ROT, and a total of 14092 new variable stars were discovered.
• Score: 8.98100769752098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Transiting Exoplanet Survey Satellite (TESS) is a wide-field all-sky survey mission designed to detect Earth-sized exoplanets. After over four years photometric surveys, data from sectors 1-57, including approximately 1,050,000 light curves with a 2-minute cadence, were collected. By cross-matching the data with Gaia's variable star catalogue, we obtained labeled datasets for further analysis. Using a random forest classifier, we performed classification of variable stars and designed distinct classification processes for each subclass, 6770 EA, 2971 EW, 980 CEP, 8347 DSCT, 457 RRab, 404 RRc and 12348 ROT were identified. Each variable star was visually inspected to ensure the reliability and accuracy of the compiled catalog. Subsequently, we ultimately obtained 6046 EA, 3859 EW, 2058 CEP, 8434 DSCT, 482 RRab, 416 RRc, and 9694 ROT, and a total of 14092 new variable stars were discovered.

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