Millions of Main-Sequence Binary Stars from Gaia BP/RP Spectra

TL;DR

This paper introduces a large catalog of main-sequence binary stars identified from Gaia BP/RP spectra, using neural networks and statistical modeling to detect binaries and analyze their properties across the Galaxy.

Contribution

The study develops a novel neural network-based forward modeling approach to identify and characterize binary stars from Gaia XP spectra, creating a comprehensive binary star catalog.

Findings

Identified 14 million binary candidates from 35 million stars within 1 kpc.

Defined a high-confidence 'golden sample' of 1 million binaries.

Enabled statistical analysis of binary properties and detection of luminous and dark companions.

Abstract

We present the main-sequence binary (MSMS) Catalog derived from Gaia Data Release 3 BP/RP (XP) spectra. Leveraging the vast sample of low-resolution Gaia XP spectra, we develop a forward modeling approach that maps stellar mass and photometric metallicity to XP spectra using a neural network. Our methodology identifies binary systems through statistical comparison of single- and binary-star model fits, enabling detection of binaries with mass ratios between 0.4 and 1.0 and flux ratios larger than 0.1. From an initial sample of 35 million stars within 1 kpc, we identify 14 million binary candidates and define a high-confidence "golden sample" of 1 million binary systems. This large, homogeneous sample enables detailed statistical analysis of binary properties across diverse Galactic environments, providing new insights into binary star formation and evolution. In addition, the $\chi^2$ comparison allows us to distinguish stars with luminous companions from single stars or binaries with dark companions, such as white dwarfs, neutron stars and black hole candidates, improving our understanding of compact object populations.