Binary stars from LRS LAMOST DR10 & Gaia DR3

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).