Vol 15, No 10

Identifying Carbon stars from the LAMOST pilot survey with the efficient manifold ranking algorithm

Jian-Min Si, Yin-Bi Li, A-Li Luo, Liang-Ping Tu, Zhi-Xin Shi, Jian-Nan Zhang, Peng Wei, Gang Zhao, Yi-Hong Wu, Fu-Chao Wu, Yong-Heng Zhao


Abstract Carbon stars are excellent kinematic tracers of galaxies and can serve as a viable standard candle, so it is worthwhile to automatically search for them in a large amount of spectra. In this paper, we apply the efficient manifold ranking algorithm to search for carbon stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) pilot survey, whose performance and robustness are verified comprehensively with four test experiments. Using this algorithm, we find a total of 183 carbon stars, and 158 of them are new findings. According to different spectral features, our carbon stars are classified as 58 C-H stars, 11 C-H star candidates, 56 C-R stars, ten C-R star candidates, 30 C-N stars, three C-N star candidates, and four C-J stars. There are also ten objects which have no spectral type because of low spectral quality, and a composite spectrum consisting of a white dwarf and a carbon star. Applying the support vector machine algorithm, we obtain the linear optimum classification plane in the J − H versus H − Ks color diagram which can be used to distinguish C-H from C-N stars with their J − H and H − Ks colors. In addition, we identify 18 dwarf carbon stars with their relatively high proper motions, and find three carbon stars with FUV detections likely have optical invisible companions by cross matching with data from the Galaxy Evolution Explorer. In the end, we detect four variable carbon stars with the Northern Sky Variability Survey, the Catalina Sky Survey and the LINEAR variability databases. According to their periods and amplitudes derived by fitting light curves with a sinusoidal function, three of them are likely semiregular variable stars and one is likely a Mira variable star.


Keywords methods: data analysis — methods: statistical — stars: carbon — binaries — stars: variables

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