21,864 Unresolved, Low-mass Binaries Identified via their Overluminosity in \textit{Gaia} DR3 and a Catalog of 347,440 Systems within 100 pc of the Sun

TL;DR

This paper develops a Gaia DR3-based method using spectral data and machine learning to identify unresolved low-mass binary stars within 100 parsecs, creating a comprehensive catalog and estimating their multiplicity fraction.

Contribution

It introduces a novel spectral and machine learning approach to distinguish unresolved binaries from single stars, expanding the understanding of low-mass star multiplicity.

Findings

Approximately 13% of low-mass stars are unresolved binaries.

Created a catalog of 347,440 systems within 100 pc.

Provided lower limits on the multiplicity fraction for stars between 0.1 and 0.7 solar masses.

Abstract

The fundamental parameters of a low-mass star can potentially be determined from its photometry and astrometry. This is complicated by the fact that 10-20 percent of low-mass stars are predicted to be equal-mass binaries. These unresolved systems appear more luminous compared to single stars with the same fundamental parameters. We present a method to differentiate binary stars from single-star main sequence K and M dwarfs using their \textit{Gaia} DR3 XP spectra. We assemble a training set of stars which have pristine astrometry and photometry, are located within 100pc of the Sun, and exclude stars with \textit{Gaia} DR3 flags suggesting they may be unequal mass systems, thereby leaving stars that are predominantly either single- or equal-mass binaries. We then iteratively train Random Forest Regression (RFR) models to predict absolute magnitude and color given the RP spectral coefficients of a star. After each model, we remove the stars that have absolute magnitudes significantly brighter than their predicted values. This method converges on a model trained only on single stars. We then use this model to identify the ``overluminous'' K and M stars in \textit{Gaia} DR3 within 100 parsecs, with some quality cuts. We find that $\sim13\%$ of the sample is significantly overluminous and assume these to be unresolved binaries. We aggregate several multiplicity surveys across different projected separations and incorporate our overluminous binaries to create a general \textit{Catalog of Systems} within 100 pc. We use this \textit{Catalog} to provide lower limits on the multiplicity fraction for stars between $0.1$ and $0.7~M_{\odot}$.