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In this work, we present a catalog of 2651 carbon stars from the fourth DataRelease (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope(LAMOST). Using an efficient machine-learning algorithm, we find out thesestars from more than seven million spectra. As a by-product, 17 carbon-enhancedmetal-poor (CEMP) turnoff star candidates are also reported in this paper, andthey are preliminarily identified by their atmospheric parameters. Except for176 stars that could not be given spectral types, we classify the other 2475carbon 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, wedivide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about90% of them are cool N-type stars as expected from previous literature. Besidespectroscopic classification, we also match these carbon stars to multiplebroadband photometries. Using ultraviolet photometry data, we find that 25carbon stars have FUV detections and they are likely to be in binary systemswith compact white dwarf companions.

Carbon stars identified from LAMOST DR4 using Machine Learning