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Employing a new machine learning method, named hierarchical extreme learningmachine (HELM) algorithm, we identified 56 hot subdwarf stars in the first datarelease (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope(LAMOST) survey. The atmospheric parameters of the stars are obtained byfitting the profiles of hydrogen (H) Balmer lines and helium (He) lines withsynthetic spectra calculated from non-Local Thermodynamic Equilibrium (NLTE)model atmospheres. Five He-rich hot subdwarf stars were found in our samplewith their log(nHe/nH) > -1 , while 51 stars are He-poor sdB, sdO and sdOBstars. We also confirmed the two He sequences of hot subdwarf stars found byEdelmann et al. (2003) in Teff - log(nHe/nH) diagram. The HELM algorithm worksdirectly on the observed spectroscopy and is able to filter out spectralproperties without supplementary photometric data. The results presented inthis study demonstrate that the HELM algorithm is a reliable method to searchfor hot subdwarf stars after a suitable training is performed, and it is alsosuitable to search for other objects which have obvious features in theirspectra or images.

Searching for Hot Subdwarf Stars in LAMOST DR1-II. Pure spectroscopic identification method for hot subdwarfs