We present MESS, a fully automated algorithm for identifying and
characterizing double-lined spectroscopic binaries (SB2) in large
databases of multi-epoch spectra. MESS extends the two-dimensional
TODCOR approach to a global multi-epoch formalism, deriving the radial
velocities (RVs) of both components at each epoch while optimizing the
templates jointly across all observations. The search is over a
continuous synthetic-spectra manifold. Single-lined spectroscopic
binaries (SB1) and single stars (S1) are handled within the same
framework by fitting one optimized template.
Model selection uses the Bayesian Information Criterion, complemented
by the Wilson relation between the two RVs for the SB2s.
We validate MESS on 1500 simulated LAMOST MRS systems with SNR=50, with
primary RV semi-amplitudes predominantly below the instrumental
resolution, achieving an overall classification accuracy of 95%.
We are applying MESS to the full LAMOST MRS red-arm DR11 data,
detecting 10^3 SB2s, including faint-secondary systems with flux ratio
(\alpha) of 0.1. The results allow us to consider the \alpha as a
function of the mass-ratio (q) of the SB2s, finding a sub-population
with surprising q \alpha relation.
