MESS: Multi-Epoch Spectroscopic Solver for Detecting Double-Lined Systems
Gil Nachmani, Simchon Faigler, Tsevi Mazeh

TL;DR
MESS is an automated multi-epoch spectroscopic algorithm that detects and characterizes double-lined and single-lined binaries by jointly optimizing templates and velocities across all observations, improving classification accuracy.
Contribution
It extends the TODCOR approach to a global multi-epoch framework, enabling simultaneous template optimization and binary classification in large spectroscopic datasets.
Findings
Achieved ~95% classification accuracy on simulated LAMOST data.
Successfully derived orbital solutions for faint-secondary SB2 systems.
Validated the method on 1500 simulated systems with SNR=50.
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…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Astrophysics and Star Formation Studies
