Carbon stars identified from LAMOST DR4 using Machine Learning
Abstract
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 out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) 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 including 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 of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside 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 they are likely to be in binary systems with compact white dwarf companions.
Keywords
Cite
@article{arxiv.1712.07784,
title = {Carbon stars identified from LAMOST DR4 using Machine Learning},
author = {Yin-Bi Li and A-Li Luo and Chang-De Du and Fang Zuo and Meng-Xin Wang and Gang Zhao and Bi-Wei Jiang and Hua-Wei Zhang and Chao Liu and Li Qin and Rui Wang and Bing Du and Yan-Xin Guo and Bo Wang and Zhan-Wen Han and Mao-sheng Xiang and Yang Huang and Bing-Qiu Chen and Jian-Jun Chen and Xiao Kong and Wen Hou and Yi-Han Song and You-Fen Wang and Ke-Fei Wu and Jian-Nan Zhang and Yong Zhang and Yue-Fei Wang and Zi-Huang Cao and Yong-Hui Hou and Yong-Heng Zhao},
journal= {arXiv preprint arXiv:1712.07784},
year = {2018}
}
Comments
72 pages, 23 figures. accepted by ApJS, lal@nao.cas.cn