MSFA-Net: An Advanced Deep Learning Model for Identifying Blue Horizontal-Branch Stars from LAMOST DR12

Abstract

Blue horizontal-branch (BHB) stars are low-mass, core helium-burning objects with nearly constant luminosities, making them powerful tracers of old, metal-poor populations and valuable standard candles for mapping the Galactic halo. However, robustly identifying BHB stars from low-resolution spectra remains challenging. We present MSFA-Net, a two-stage framework developed for the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR12. By combining multi-scale convolutions with a soft frequency attention mechanism, MSFA-Net learns discriminative representations in both the wavelength domain and the Fourier-frequency domain. On the test set, the framework achieves a precision of 94.67% in the initial multiclass screening and 98.07% in the subsequent binary refinement. Applying the pipeline to LAMOST DR12, we retrieve 27,853 BHB candidate spectra. After spectral deduplication and removal of previously known objects, we identify 3583 new BHB stars, confirmed via Balmer-line profile fitting. We further estimate atmospheric parameters (Teff, log g, and [Fe/H]) using the machine-learning-based SLAM model and examine their distributions. A non-negligible subset shows unusually high log g and/or metallicities, which we interpret primarily as inference-related systematics rather than intrinsic properties. Photometric cross-matching with Gaia DR3 and color-magnitude diagrams provide an additional consistency check for the sample. The resulting catalog substantially enlarges the spectroscopically confirmed BHB sample from LAMOST and offers a homogeneous data set for studies of Galactic-halo structure and stellar populations.