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We developed a convolutional neural network (CNN) model to distinguish thedouble-lined spectroscopic binaries (SB2s) from others based on single exposuremedium-resolution spectra ($R\sim 7,500$). The training set consists of a largeset of mock spectra of single stars and binaries synthesized based on the MISTstellar evolutionary model and ATLAS9 atmospheric model. Our model reaches anovel theoretic false positive rate by adding a proper penalty on the negativesample (e.g., 0.12\% and 0.16\% for the blue/red arm when the penalty parameter$\Lambda=16$). Tests show that the performance is as expected and favorsFGK-type Main-sequence binaries with high mass ratio ($q \geq 0.7$) and largeradial velocity separation ($\Delta v \geq 50\,\mathrm{km\,s^{-1}}$). Althoughthe real false positive rate can not be estimated reliably, validating oneclipsing binaries identified from Kepler light curves indicates that our modelpredicts low binary probabilities at eclipsing phases (0, 0.5, and 1.0) asexpected. The color-magnitude diagram also helps illustrate its feasibility andcapability of identifying FGK MS binaries from spectra. We conclude that thismodel is reasonably reliable and can provide an automatic approach to identifySB2s with period $\lesssim 10$ days. This work yields a catalog of binaryprobabilities for over 5 million spectra of 1 million sources from the LAMOSTmedium-resolution survey (MRS), and a catalog of 2198 SB2 candidates whosephysical properties will be analyzed in our following-up paper. Data productsare made publicly available at the journal as well as our Github website.

the astrophysical journal. supplement series/astrophysical journal. supplement series

The Spectroscopic Binaries from LAMOST Medium-Resolution Survey (MRS).  I. Searching for Double-lined Spectroscopic Binaries (SB2s) with  Convolutional Neural Network