Searching for Hot Subdwarf Stars from the LAMOST Spectra. III. Classification of Hot Subdwarf Stars in the Fourth Data Release of LAMOST Using a Deep Learning 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 spectra of hot subdwarf stars can provide detailed information on stellar atmospheric parameters, such as the effective temperature, gravity, and abundances of helium, which can help clarify the astrophysical and statistical properties of hot subdwarf stars. These properties provide important constraints on the theoretical models of stars. The identification of hot subdwarf stars from the spectral data obtained by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) can significantly increase the sample size and help us to better understand the nature of hot subdwarf stars. In this study, we propose a new method to select hot subdwarf stars from LAMOST spectra using convolutional neural networks and a support vector machine (CNN+SVM). By applying CNN+SVM to sample data selected from LAMOST Data Release 4 we obtain an F1 score of 76.98%. A comparison with other machine-learning algorithms, such as linear discriminant analysis and k-nearest neighbors, demonstrates that an approach based on CNN+SVM obtains better results than the others. Therefore it is a method well suited to the problem of searching for hot subdwarf stars in large spectroscopic surveys. Finally, we include an extensive discussion on how we determined the optimal hyperparameters of our proposed method.