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

Carbon starsare 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 starsfrom 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 starsare classified as 58 C-H stars, 11 C-H starcandidates, 56 C-R stars, ten C-R starcandidates, 30 C-N stars, three C-N starcandidates, 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 - K-s color diagram which can be used to distinguish C-H from C-N starswith their J - H and H - K-s colors. In addition, we identify 18 dwarf carbon starswith their relatively high proper motions, and find three carbon starswith 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 starswith 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 starsand one is likely a Mira variable star.