用机器学习分析105万条恒星光变曲线，发现1.4万颗新变星。
Using machine learning method for variable star classification using the TESS Sectors 1-57 data
- 基于随机森林分类器，针对不同变星类型设计专用识别流程。
- 从TESS前57个区数据中识别出14092颗新变星，准确率高。
- 适合天体物理、天文数据挖掘及机器学习应用研究者阅读。
凌日系外行星巡天卫星（TESS）是一项宽视场全天巡天任务，旨在探测类地系外行星。经过四年多的光度巡天，已收集到第1至57区的数据，包含约105万条2分钟采样间隔的光变曲线。通过与盖亚变星目录交叉匹配，获得了可用于分析的标注数据集。采用随机森林分类器对变星进行分类，并为每种子类设计了独立的分类流程，共识别出6770颗EA型、2971颗EW型、980颗CEP型、8347颗DSCT型、457颗RRab型、404颗RRc型和12348颗ROT型变星。每颗变星均经人工目视检查以确保编目可靠性和准确性。最终获得6046颗EA型、3859颗EW型、2058颗CEP型、8434颗DSCT型、482颗RRab型、416颗RRc型和9694颗ROT型变星，总计发现14092颗新变星。
原文摘要 · Abstract (English)
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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