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Minute-cadence obser v ations of the LAMOST Fields with the TMTS–V. Machine learning classification of TMTS catalogues of periodic variable stars

文献类型：期刊论文

作者Guo, Fangzhou14; Lin, Jie12,13,14; Wang, Xiaofeng11,14; Chen, Xiaodian10; Li, Tanda7,8,9; Chen, Liyang14; Xia, Qiqi14; Mo, Jun14; Xi, Gaobo14; Zhang, Jicheng8
刊名Monthly Notices of the Royal Astronomical Society
出版日期2024-03-01
卷号528期号:4页码:6997-7015
关键词surveys binaries: eclipsing stars: oscillations (including pulsations) stars: variables: Scuti
ISSN号0035-8711
DOI10.1093/mnras/stae404
产权排序第12完成单位
文献子类Article
英文摘要Periodic variables are always of great scientific interest in astrophysics. Thanks to the rapid advancement of modern large-scale time-domain surveys, the number of reported variable stars has experienced substantial growth for several decades, which significantly deepened our comprehension of stellar structure and binary evolution. The Tsinghua University-Ma Huateng Telescopes for Survey (TMTS) has started to monitor the LAMOST sky areas since 2020, with a cadence of 1 min. During the period from 2020 to 2022, this survey has resulted in densely sampled light curves for ∼30 000 variables of the maximum powers in the Lomb–Scargle periodogram above the 5 σ threshold. In this paper, we classified 11 638 variable stars into six main types using XGBOOST and Random Forest classifiers with accuracies of 98.83 percent and 98.73 percent, respectively. Among them, 5301 (45.55 percent) variables are newly discovered, primarily consisting of δ Scuti stars, demonstrating the capability of TMTS in searching for short-period variables. We cross-matched the catalogue with Gaia’s second Data Release and LAMOST’s seventh Data Release to obtain important physical parameters of the variables. We identified 5504 δ Scuti stars (including 4876 typical δ Scuti stars and 628 high-amplitude δ Scuti stars), 5899 eclipsing binaries (including EA-, EB-, and EW-type), and 226 candidates of RS Canum Venaticorum. Leveraging the metal abundance data provided by LAMOST and the Galactic latitude, we discovered eight candidates of SX Phe stars within the class of ‘ δ Scuti stars’. Moreover, with the help of Gaia colour–magnitude diagram, we identified nine ZZ Ceti stars. © 2024 The Author(s).
学科主题天文学
URL标识查看原文
出版地GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
WOS关键词DELTA-SCUTI STARS ; ASAS-SN CATALOG ; RR LYRAE STARS ; AUTOMATED CLASSIFICATION ; CONTACT BINARIES ; GALACTIC BULGE ; SX PHOENICIS ; WHITE-DWARF ; GAIA ; EVOLUTION
资助项目Gordon and Betty Moore Foundation[GBMF5490];National Natural Science Foundation of China[12033003];National Natural Science Foundation of China[12288102];National Natural Science Foundation of China[12303054]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001184933600002
出版者OXFORD UNIV PRESS
资助机构National Science Foundation of China[12033003, 12288102] ; National Science Foundation of China ; Ma Huateng Foundation[DZ:BS202002] ; Scholar Program of Beijing Academy of Science and Technology ; New Cornerstone Science Foundation ; Cyrus Chun Ying Tang Foundations[12373031, 12090040, 12090042, U2031203] ; National Natural Science Foundation of China[202302AN360001] ; International Centre of Supernovae, Yunnan Key Laboratory ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; China Space Station Telescope (CSST) project ; Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST) ; National Development and Reform Commission ; Chinese Academy of Sciences ; Optical Gravitational Lensing Experiment (OGLE)[MAESTRO 2014/14/A/ST9/00121] ; National Science Centre, Poland[GBMF5490] ; Ohio State University by the Gordon and Betty Moore Foundation[AST-1908570] ; National Science Foundation ; Massachusetts, USA
源URL[http://ir.ynao.ac.cn/handle/114a53/26494]  
专题云南天文台_丽江天文观测站（南方基地）
云南天文台_中国科学院天体结构与演化重点实验室
作者单位1.International Centre of Superno vae, Yunnan Key Laboratory, Kunming, 650216, China
2.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming, 650216, China;
3.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650216, China;
4.The School of Physics and Astronomy, Tel Aviv University, Tel Aviv, 69978, Israel;
5.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, 100049, China;
6.National Astronomical Observatories of China, Chinese Academy of Sciences, Beijing, 100012, China;
7.School of Physics and Astronomy, The University of Birmingham, Birmingham, B15 2TT, United Kingdom;
8.Department of Astronomy, Beijing Normal University, Beijing, 100875, China;
9.Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University, Beijing, 102206, China;
10.CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, China;
推荐引用方式
GB/T 7714		 Guo, Fangzhou,Lin, Jie,Wang, Xiaofeng,et al. Minute-cadence obser v ations of the LAMOST Fields with the TMTS–V. Machine learning classification of TMTS catalogues of periodic variable stars[J]. Monthly Notices of the Royal Astronomical Society,2024,528(4):6997-7015.
APA Guo, Fangzhou.,Lin, Jie.,Wang, Xiaofeng.,Chen, Xiaodian.,Li, Tanda.,...&蔡永志.(2024).Minute-cadence obser v ations of the LAMOST Fields with the TMTS–V. Machine learning classification of TMTS catalogues of periodic variable stars.Monthly Notices of the Royal Astronomical Society,528(4),6997-7015.
MLA Guo, Fangzhou,et al."Minute-cadence obser v ations of the LAMOST Fields with the TMTS–V. Machine learning classification of TMTS catalogues of periodic variable stars".Monthly Notices of the Royal Astronomical Society 528.4(2024):6997-7015.

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