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Hot subdwarf stars are very important for understanding stellar evolution,stellar astrophysics, and binary star systems. Identifying more such stars canhelp us better understand their statistical distribution, properties, andevolution. In this paper, we present a new method to search for hot subdwarfstars in photometric data (b, y, g, r, i, z) using a machine learningalgorithm, graph neural network, and Gaussian mixture model. We use a Gaussianmixture model and Markov distance to build the graph structure, and on thegraph structure, we use a graph neural network to identify hot subdwarf starsfrom 86 084 stars, when the recall, precision, and f1 score are maximized onthe original, weight and synthetic minority oversampling technique datasets.Finally, from 21 885 candidates, we selected approximately 6 000 stars thatwere the most similar to the hot subdwarf star.

Identifying hot subdwarf stars from photometric data using Gaussian mixture model and graph neural network