Autonomous Disentangling for Spectroscopic Surveys
Rhys Seeburger, Hans-Walter Rix, Kareem El-Badry, Maosheng Xiang,, Morgan Fouesneau

TL;DR
This paper introduces an automated spectral disentangling method tailored for large spectroscopic surveys with few epochs and moderate resolution, enabling analysis of binary stars in vast stellar datasets.
Contribution
It presents a novel disentangling implementation that handles large survey data with limited epochs and resolution, including automatic initialization and regularisation techniques.
Findings
Effective spectral recovery demonstrated on simulated data.
Verified on binary systems 'Unicorn' and 'Giraffe'.
Applicable to large surveys like SDSS-V for massive star analysis.
Abstract
A suite of spectroscopic surveys is producing vast sets of stellar spectra with the goal of advancing stellar physics and Galactic evolution by determining their basic physical properties. A substantial fraction of these stars are in binary systems, but almost all large-survey modeling pipelines treat them as single stars. For sets of multi-epoch spectra, spectral disentangling is a powerful technique to recover or constrain the individual components' spectra of a multiple system. So far, this approach has focused on small samples or individual objects, usually with high resolution () spectra and many epochs (). Here, we present a disentangling implementation that accounts for several aspects of few-epoch spectra from large surveys: that vast sample sizes require automatic determination of starting guesses; that some of the most extensive spectroscopic…
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