中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 云南天文台
  3. 中国科学院云南天文台
  4. 大样本恒星演化研究组
Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX

文献类型：期刊论文

作者Li JJ(李佳佳)1,2; Xiong JP(熊建萍)1; Tian, Zhi-jia3; Liu, Chao2,4; Han ZW(韩占文)1,2,5,6; Chen XF(陈雪飞)1,2,5,6
刊名ASTRONOMICAL JOURNAL
出版日期2025-04-01
卷号169期号:4
ISSN号0004-6256
DOI10.3847/1538-3881/adb95e
产权排序第1完成单位
文献子类Article
英文摘要The statistical characteristics of double main-sequence (MS) binaries are essential for investigating star formation, binary evolution, and population synthesis. Our previous study proposed a machine learning-based method to identify MS binaries from MS single stars using mock data from the Chinese Space Station Telescope (CSST). We further utilized detection efficiencies and an empirical mass ratio distribution to estimate the binary fraction within the sample. To further validate the effectiveness of this method, we conducted a more realistic sample simulation, incorporating additional factors such as metallicity, extinction, and photometric errors from CSST simulations. The detection efficiency for binaries with mass ratios between 0.2 and 0.7 reached over 80%. We performed a detailed observational validation using the data selected from the Gaia Sky Survey and Galaxy Evolution Explorer. The detection efficiency for MS binaries in the observed sample was 65%. The binary fraction can be inferred with high precision for a set of observed samples, based on accurate empirical mass ratio distribution.
学科主题天文学 ; 恒星与银河系
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词SN ECLIPSING BINARIES ; VALUE-ADDED CATALOG ; DATA RELEASE 3 ; STELLAR MULTIPLICITY ; WIDE BINARIES ; STARS ; APOGEE ; EVOLUTION ; PARAMETERS ; PRECISION
资助项目MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12288102]; MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12125303]; MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12090040/3]; National Natural Science Foundation of China[2021YFA1600403]; National Key R&D Program of China[202201BC070003]; Yunnan Fundamental Research Projects[202302AN360001]; International Centre of Supernovae, Yunnan Key Laboratory[202305AB350003]; Yunnan Revitalization Talent Support Program-Science & Technology Champion Project[CMS-CSST-2021-A10]; China Manned Space Project
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001467576000001
出版者IOP Publishing Ltd
资助机构MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[12288102, 12125303, 12090040/3] ; National Natural Science Foundation of China[2021YFA1600403] ; National Key R&D Program of China[202201BC070003] ; Yunnan Fundamental Research Projects[202302AN360001] ; International Centre of Supernovae, Yunnan Key Laboratory[202305AB350003] ; Yunnan Revitalization Talent Support Program-Science & Technology Champion Project[CMS-CSST-2021-A10] ; China Manned Space Project
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/28260]  
专题云南天文台_大样本恒星演化研究组
作者单位1.Yunnan Observatories, Chinese Academy of Sciences, 396 Yangfangwang, Guandu District, Kunming, 650216, People's Republic of China; lijiajia@ynao.ac.cn, cxf@ynao.ac.cn;
2.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing, 100049, People's Republic of China;
3.Yunnan University, Kunming 650091, People's Republic of China;
4.Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, 100101, People's Republic of China;
5.Center for Astronomical Mega-Science, Chinese Academy of Sciences, Beijing 100012, People's Republic of China;
6.International Centre of Supernovae, Yunnan Key Laboratory, Kunming 650216, People's Republic of China
推荐引用方式
GB/T 7714		 Li JJ,Xiong JP,Tian, Zhi-jia,et al. Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX[J]. ASTRONOMICAL JOURNAL,2025,169(4).
APA 李佳佳,熊建萍,Tian, Zhi-jia,Liu, Chao,韩占文,&陈雪飞.(2025).Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX.ASTRONOMICAL JOURNAL,169(4).
MLA 李佳佳,et al."Identify Main-sequence Binaries from the Chinese Space Station Telescope Survey with Machine Learning. II. Based on Gaia and GALEX".ASTRONOMICAL JOURNAL 169.4(2025).

入库方式： OAI收割

来源：云南天文台

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。