A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning

Author:

Qiao 乔 Peiyun 佩云ORCID,Xu 许 Tingting 婷婷ORCID,Wang 王 Feng 锋ORCID,Mei 梅 Ying 盈ORCID,Deng 邓 Hui 辉ORCID,Tan 谈 Lei 磊ORCID,Liu 刘 Chao 超ORCID

Abstract

Abstract Identifying and classifying variable stars is essential to time-domain astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) acquired a large amount of spectral data. However, there is no corresponding variable source-related information in the data, constraining LAMOST data utilization for scientific research. In this study, we systematically investigated variable source classification methods for LAMOST data. We constructed a 10-class classification model using three mainstream machine-learning methods. Through performance comparison, we chose the LightGBM and XGBoost models. We further identified variable source candidates in the r band in LAMOST DR9 and obtained 281,514 variable source candidates with probabilities greater than 95%. Subsequently, we filtered out the sources of periodic variable sources using the generalized Lomb–Scargle periodogram and classified these periodic variable sources using the classification model. Finally, we propose a reliable periodic variable star catalog containing 176,337 stars with specific types.

Funder

National SKA Program of China

National Science Foundation of China

Guangzhou Science and Technology Funds

Publisher

American Astronomical Society

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