Abstract 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. Template optimization searches a continuous synthetic-spectra manifold spanning an eight-dimensional parameter space: effective temperature, surface gravity, and rotational broadening for each star, together with a common metallicity and the flux ratio. Single-lined spectroscopic binaries (SB1) and single stars (S1) are handled within the same framework by fitting one optimized template, with either epoch-dependent RVs (SB1) or a single shared RV (S1). Model selection among S1/ SB1/ SB2 uses the Bayesian information criterion with an effective sample size that accounts for intra-spectrum correlations, and is complemented by the Wilson relation between the two RVs to infer the mass ratio and systemic velocity without a full orbital solution. We validate MESS on 1500 simulated LAMOST MRS systems (SNR=50), with primary RV semi-amplitudes predominantly below the instrumental resolution, achieving an overall classification accuracy of ∼95%. We also derive full orbital solutions for two SB2 systems detected in our LAMOST analysis, including a faint-secondary case with flux ratio ∼0. 1, and present example outputs for one SB1 and three constant-velocity stars. A companion paper will report the survey-wide application to LAMOST DR10 and the resulting SB1/ SB2 catalogs.
Nachmani et al. (2026) studied this question.