The Spectroscopic Binaries from LAMOST Medium-Resolution Survey (MRS).
  I. Searching for Double-lined Spectroscopic Binaries (SB2s) with
  Convolutional Neural Network
  We developed a convolutional neural network (CNN) model to distinguish the
double-lined spectroscopic binaries (SB2s) from others based on single exposure
medium-resolution spectra ($R\sim 7,500$). The training set consists of a large
set of mock spectra of single stars and binaries synthesized based on the MIST
stellar evolutionary model and ATLAS9 atmospheric model. Our model reaches a
novel theoretic false positive rate by adding a proper penalty on the negative
sample (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 favors
FGK-type Main-sequence binaries with high mass ratio ($q \geq 0.7$) and large
radial velocity separation ($\Delta v \geq 50\,\mathrm{km\,s^{-1}}$). Although
the real false positive rate can not be estimated reliably, validating on
eclipsing binaries identified from Kepler light curves indicates that our model
predicts low binary probabilities at eclipsing phases (0, 0.5, and 1.0) as
expected. The color-magnitude diagram also helps illustrate its feasibility and
capability of identifying FGK MS binaries from spectra. We conclude that this
model is reasonably reliable and can provide an automatic approach to identify
SB2s with period $\lesssim 10$ days. This work yields a catalog of binary
probabilities for over 5 million spectra of 1 million sources from the LAMOST
medium-resolution survey (MRS), and a catalog of 2198 SB2 candidates whose
physical properties will be analyzed in our following-up paper. Data products
are made publicly available at the journal as well as our Github website.
