CASIA OpenIR
Carbon Stars Identified from LAMOST DR4 Using Machine Learning
Li,Yin-Bi1; Luo,A-Li1; Du,Chang-De1,2,3; Zuo,Fang1; Wang,Meng-Xin1,2; Zhao,Gang1; Jiang,Bi-Wei4; Zhang,Hua-Wei5; Liu,Chao1; Qin,Li1,2; Wang,Rui1,2; Du,Bing1,2; Guo,Yan-Xin1,2; Wang,Bo6; Han,Zhan-Wen6; Xiang,Mao-Sheng1; Huang,Yang7; Chen,Bing-Qiu7; Chen,Jian-Jun1; Kong,Xiao1,2; Hou,Wen1; Song,Yi-Han1; Wang,You-Fen1; Wu,Ke-Fei1,2; Zhang,Jian-Nan1; Zhang,Yong8; Wang,Yue-Fei8; Cao,Zi-Huang1; Hou,Yong-Hui8; Zhao,Yong-Heng1
Source PublicationThe Astrophysical Journal Supplement Series
ISSN0067-0049
2018-02-05
Volume234Issue:2
AbstractAbstract In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find these stars from more than 7 million spectra. As a by-product, 17 carbon-enhanced metal-poor turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes: 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes, C-J(H), C-J(R), and C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Besides spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and that they are likely to be in binary systems with compact white dwarf companions.
Keywordcatalogs methods: data analysis methods: statistical stars: carbon surveys
DOI10.3847/1538-4365/aaa415
Language英语
WOS IDIOP:0067-0049-234-2-aaa415
PublisherThe American Astronomical Society
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24472
Collection中国科学院自动化研究所
Affiliation1.Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, People's Republic of China; lal@bao.ac.cn
2.University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
3.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, People's Republic of China
4.Department of Astronomy, Beijing Normal University, Beijing 100875, People's Republic of China
5.Department of Astronomy, School of Physics, Peking University, Beijing 100871, People's Republic of China
6.Key Laboratory for the Structure and Evolution of Celestial Objects, Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People's Republic of China
7.South-Western Institute for Astronomy Research, Yunnan University, Kunming 650500, People's Republic of China
8.Nanjing Institute of Astronomical Optics & Technology, National Astronomical Observatories, Chinese Academy of Sciences, Nanjing 210042, People's Republic of China
Recommended Citation
GB/T 7714
Li,Yin-Bi,Luo,A-Li,Du,Chang-De,et al. Carbon Stars Identified from LAMOST DR4 Using Machine Learning[J]. The Astrophysical Journal Supplement Series,2018,234(2).
APA Li,Yin-Bi.,Luo,A-Li.,Du,Chang-De.,Zuo,Fang.,Wang,Meng-Xin.,...&Zhao,Yong-Heng.(2018).Carbon Stars Identified from LAMOST DR4 Using Machine Learning.The Astrophysical Journal Supplement Series,234(2).
MLA Li,Yin-Bi,et al."Carbon Stars Identified from LAMOST DR4 Using Machine Learning".The Astrophysical Journal Supplement Series 234.2(2018).
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