Searching for Hot Subdwarf Stars from the LAMOST Spectra. I. Method

Abstract

Abstract Hot subdwarf stars are core He burning stars located at the blue end of the horizontal branch, which is also known as the extreme horizontal branch. The study of hot subdwarf stars is important for understanding stellar astrophysics, globular clusters, and galaxies. Presently, some problems associated with hot subdwarf stars are still unclear. To better study the properties of these stars, we should find more hot subdwarf stars to enlarge the sample size. The traditional method of searching for hot subdwarfs from the large data sets is based on the color cuts followed by visual inspection. This method is not suitable for the data set without homogeneous colors, such as the spectra obtained by the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST). In this paper, we present a new method of searching for hot subdwarf stars in large spectroscopic surveys using a machine learning algorithm, the hierarchical extreme learning machine (HELM) algorithm. We have applied the HELM algorithm to the spectra from the LAMOST survey, and classification errors are considerably small: for the single hot subdwarf stars, accuracy = 0.92 and efficiency - 0.96; and for the hot subdwarf binaries, accuracy = 0.80 and efficiency = 0.71. A comparison of the HELM and other popular algorithms shows that HELM is accurate and efficient in classifying hot subdwarf stars. This method provides a new tool for searching for hot subdwarf stars in large spectroscopic surveys.