The Discovery of New Hot Subdwarf Stars in LAMOST DR12

Abstract Hot subdwarf stars are important celestial objects in the study of stellar physics, but the population remains limited. The LAMOST DR12-V1, released in March 2025, is currently the world's largest spectroscopic database, holding great potential for the search of hot subdwarf stars. In this study, we propose a two-stage deep learning model called the hot subdwarf network (HsdNet), which integrates multiple advanced techniques, comprising a binary classification model in stage one and a five-class classification model in stage two. HsdNet not only achieves high precision with 94.33% and 94.00% in the binary and the five-class classification stages, respectively, but also quantifies the predicted uncertainty, enhancing the interpretability of the classification results through visualizing the model's key focus regions. We applied HsdNet to the 601,217 spectra from the LAMOST DR12-V1 database, conducting a two-stage search for hot subdwarf candidates. In stage one, we initially identified candidates using the binary classification model. In stage two, the five-class classification model was used to further refine these candidates. Finally, we manually confirmed 1008 newly identified hot subdwarf stars. The distribution of their atmospheric parameters is consistent with that of known hot subdwarf stars. These efforts are expected to significantly advance the research on hot subdwarf stars.