Machine Learning to Search for Accreting Neutron Star Binary Candidates Using Chinese Space Station Telescope Photometric System
文献类型:期刊论文
作者 | Lan SY(兰顺义)1,2,3; Ji KF(季凯帆)1; Meng XC(孟祥存)1,2 |
刊名 | RESEARCH IN ASTRONOMY AND ASTROPHYSICS |
出版日期 | 2022-12-01 |
卷号 | 22期号:12 |
ISSN号 | 1674-4527 |
关键词 | stars: neutron X-rays: binaries methods: analytical |
DOI | 10.1088/1674-4527/ac9e92 |
产权排序 | 第1完成单位 |
文献子类 | Article |
英文摘要 | Accreting neutron star binary (ANSB) systems can provide some important information about neutron stars (NSs), especially on the structure and the equation of state of NSs. However, only a few ANSBs are known so far. The upcoming Chinese Space Station Telescope (CSST) provides an opportunity to search for a large number of ANSB candidates. We aim to investigate whether or not a machine learning method may efficiently search for ANSBs based on CSST photometric system. In this paper, we generate some ANSBs and normal binaries under CSST photometric system by binary evolution and binary population synthesis method and use a machine learning method to train a classification model. We consider the classical multi-color disk and the irradiated accretion disk, then compare their effects on the classification results. We find that no matter whether the X-ray reprocessing effect is included or not, the machine learning classification accuracy is always very high, i.e., higher than 96%. If a significant magnitude difference exists between the accretion disk and the companion of an ANSB, machine learning may not distinguish it from some normal stars such as massive main sequence stars, white dwarf binaries, etc. False classifications of the ANSBs and the normal stars highly overlap in a color-color diagram. Our results indicate that machine learning would be a powerful way to search for potential ANSB candidates from the CSST survey. |
学科主题 | 天文学 ; 恒星与银河系 ; 恒星形成与演化 |
URL标识 | 查看原文 |
出版地 | 20A DATUN RD, CHAOYANG, BEIJING, 100012, PEOPLES R CHINA |
WOS关键词 | X-RAY BINARIES ; DISC INSTABILITY MODEL ; LOW-MASS ; MILLISECOND PULSAR ; INDUCED COLLAPSE ; IA SUPERNOVAE ; EVOLUTION ; PROGENITORS ; CATALOG |
资助项目 | National Key Ramp ; D Program of China[2021YFA1600403] ; National Natural Science Foundation of China[11973080] ; National Natural Science Foundation of China[11733008] ; China Manned Space Project[CMS-CSST-2021-B07] ; Yunnan Ten Thousand Talents Plan-Young & Elite Talents Project ; CAS Light of West China Program |
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:000897926300001 |
资助机构 | National Key Ramp ; D Program of China[2021YFA1600403] ; National Natural Science Foundation of China[11973080, 11733008] ; China Manned Space Project[CMS-CSST-2021-B07] ; Yunnan Ten Thousand Talents Plan-Young & Elite Talents Project ; CAS Light of West China Program |
版本 | 出版稿 |
源URL | [http://ir.ynao.ac.cn/handle/114a53/25692] |
专题 | 云南天文台_大样本恒星演化研究组 云南天文台_中国科学院天体结构与演化重点实验室 天文技术实验室 |
通讯作者 | Lan SY(兰顺义); Meng XC(孟祥存) |
作者单位 | 1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China; lanshunyi@ynao.ac.cn, xiangcunmeng@ynao.ac.cn; 2.Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650216, China; 3.University of Chinese Academy of Sciences, Beijing 100049, China |
推荐引用方式 GB/T 7714 | Lan SY,Ji KF,Meng XC. Machine Learning to Search for Accreting Neutron Star Binary Candidates Using Chinese Space Station Telescope Photometric System[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2022,22(12). |
APA | Lan SY,Ji KF,&Meng XC.(2022).Machine Learning to Search for Accreting Neutron Star Binary Candidates Using Chinese Space Station Telescope Photometric System.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,22(12). |
MLA | Lan SY,et al."Machine Learning to Search for Accreting Neutron Star Binary Candidates Using Chinese Space Station Telescope Photometric System".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 22.12(2022). |
入库方式: OAI收割
来源:云南天文台
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