中国科学院机构知识库网格系统: Identifying Symbiotic Stars with Machine Learning

Identifying Symbiotic Stars with Machine Learning

文献类型：期刊论文


作者	Jia, Yongle 2; Guo, Sufen 2; Zhu, Chunhua2 ; Li, Lin2 ; Ma, Mei 2; Lu, Guoliang 1
刊名	RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期	2023-10-01
卷号	23 期号:10 页码:14
ISSN号	1674-4527
关键词	(stars:) binaries: symbiotic techniques: spectroscopic (stars:) binaries: spectroscopic methods: data analysis
DOI	10.1088/1674-4527/ace9b2
通讯作者	Guo, Sufen(guosufen@xju.edu.cn)
英文摘要	Symbiotic stars are interacting binary systems, making them valuable for studying various astronomical phenomena, such as stellar evolution, mass transfer, and accretion processes. Despite recent progress in the discovery of symbiotic stars, a significant discrepancy between the observed population of symbiotic stars and the number predicted by theoretical models. To bridge this gap, this study utilized machine learning techniques to efficiently identify new symbiotic star candidates. Three algorithms (XGBoost, LightGBM, and Decision Tree) were applied to a data set of 198 confirmed symbiotic stars and the resulting model was then used to analyze data from the LAMOST survey, leading to the identification of 11,709 potential symbiotic star candidates. Out of these potential symbiotic star candidates listed in the catalog, 15 have spectra available in the Sloan Digital Sky Survey (SDSS) survey. Among these 15 candidates, two candidates, namely V* V603 Ori and V* GN Tau, have been confirmed as symbiotic stars. The remaining 11 candidates have been classified as accreting-only symbiotic star candidates. The other two candidates, one of which has been identified as a galaxy by both SDSS and LAMOST surveys, and the other identified as a quasar by SDSS survey and as a galaxy by LAMOST survey.
WOS关键词	ALL-SKY SURVEY ; SPECTROSCOPIC OBSERVATIONS ; SPECTRAL CLASSIFICATION ; POPULATION SYNTHESIS ; DECISION TREES ; MASS-TRANSFER ; DATA RELEASE ; ASTROPY ; IDENTIFICATION ; CLASSIFIERS
资助项目	Natural Science Foundation of Xinjiang[2021D01C075] ; National Natural Science Foundation of China[12163005] ; National Natural Science Foundation of China[12003025] ; National Natural Science Foundation of China[U2031204] ; National Natural Science Foundation of China[11863005] ; National Natural Science Foundation of China[12288102] ; China Manned Space Project[CMS-CSST-2021-A10] ; Scientific Research Program of the Higher Education Institution of Xinjiang[XJEDU2022P003] ; Astronomical Big Data Joint Research Center ; National Aeronautics and Space Administration ; National Development and Reform Commission ; U.S. Department of Energy Office of Science ; Brazilian Participation Group ; Carnegie Institution for Science ; Carnegie Mellon University ; Center for Astrophysics \| Harvard amp; Smithsonian ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Korean Participation Group ; Lawrence Berkeley National Laboratory ; Leibniz Institut fuer Astrophysik Potsdam (AIP) ; Max-Planck-Institut fuer Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fuer Astrophysik (MPA Garching) ; Max-Planck-Institut fuer Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University
WOS研究方向	Astronomy & Astrophysics
语种	英语
出版者	NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
WOS记录号	WOS:001073964400001
资助机构	Natural Science Foundation of Xinjiang ; National Natural Science Foundation of China ; China Manned Space Project ; Scientific Research Program of the Higher Education Institution of Xinjiang ; Astronomical Big Data Joint Research Center ; National Aeronautics and Space Administration ; National Development and Reform Commission ; U.S. Department of Energy Office of Science ; Brazilian Participation Group ; Carnegie Institution for Science ; Carnegie Mellon University ; Center for Astrophysics \| Harvard amp; Smithsonian ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Korean Participation Group ; Lawrence Berkeley National Laboratory ; Leibniz Institut fuer Astrophysik Potsdam (AIP) ; Max-Planck-Institut fuer Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fuer Astrophysik (MPA Garching) ; Max-Planck-Institut fuer Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University
源URL	[http://ir.xao.ac.cn/handle/45760611-7/5445]
专题	研究单元未命名
通讯作者	Guo, Sufen
作者单位	1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 2.Xinjiang Univ, Sch Phys Sci & Technol, Urumqi 830046, Peoples R China
推荐引用方式 GB/T 7714	Jia, Yongle,Guo, Sufen,Zhu, Chunhua,et al. Identifying Symbiotic Stars with Machine Learning[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2023,23(10):14.
APA	Jia, Yongle,Guo, Sufen,Zhu, Chunhua,Li, Lin,Ma, Mei,&Lu, Guoliang.(2023).Identifying Symbiotic Stars with Machine Learning.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,23(10),14.
MLA	Jia, Yongle,et al."Identifying Symbiotic Stars with Machine Learning".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 23.10(2023):14.

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Identifying Symbiotic Stars with Machine Learning

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