2025ApJS..277...15J

Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a data set that distinguishes binaries from single main-sequence stars based on their positions on the Hertzsprung-Russell diagram. Specifically, the training data labels stars with mass ratios between approximately 0.71 and 0.93 as intermediate-mass-ratio binaries, while excluding those beyond this range. The network achieves high accuracy with an area under the receiver operating characteristic curve of 0.949 on the test set. Its performance is further validated against known eclipsing binaries (97% detection rate) and binary stars identified by radial velocity variations (92% detection rate). Applying the trained CNN to a sample of one million main-sequence stars from LAMOST DR10 and Gaia DR3 yields a catalog of 468,634 binary stars, which are mainly intermediate-mass-ratio binaries given the training data. This catalog includes 115 binary stars located beyond 10 kpc from the Sun and 128 crossmatched with known exoplanet hosts from the NASA Exoplanet Archive. This new catalog provides a valuable resource for future research on the properties, formation, and evolution of binary systems, particularly for statistically characterizing large populations.