Double-lined Spectroscopic Binaries Identified in LAMOST DR11 with a Deep Learning Model

Abstract Double-lined spectroscopic binaries (SB2s) hold significant importance for understanding stellar formation and evolution. With the release of massive spectra, deep learning techniques have achieved major breakthroughs in the search of spectroscopic binaries. However, traditional deep learning models are unable to provide predicted uncertainty, which raises concerns about the reliability of the results. In this study, we propose a model called SB 2 Net to identify SB2s in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Medium-resolution Survey (LAMOST-MRS) and estimate predicted uncertainty. SB 2 Net integrates multiple techniques, enabling the efficient extraction of critical features from the cross-correlation function and providing high-confidence classification results. On the test set, our model achieved a precision of 99.53%, with 99% of the samples having epistemic uncertainty less than 0.0017 and aleatoric uncertainty less than 0.0681. By applying SB 2 Net to 2,526,141 spectra from LAMOST-MRS data release 11, we identified 7711 SB2 candidates, of which 3037 were newly discovered. Additionally, we estimated radial velocities (RVs) and RV errors for all candidate spectra, while validating the consistency of the candidates properties with other catalogs through the color–magnitude diagram. Finally, by fitting the RV curves, we obtained 246 well-fitted orbital solutions with most systems having mass ratio converge to 1. The period–eccentricity relationship reveals that short-period binaries tend to exhibit more circular orbits. In the future, we plan to separate the composite spectra of SB2s to study the properties and atmospheric parameters of the component stars.