Millions of main-sequence binary stars from <i>Gaia</i> BP/RP spectra

We present an extensive catalog of likely main-sequence main-sequence (MSMS) binary stars systems derived from Gaia Data Release 3 BP/RP (XP) spectra through the comparison of single- and binary-star model fits. Leveraging the large sample of low-resolution Gaia XP spectra, we used a neural network to build a forward model for spectral luminosities of single stars as a function of stellar mass and photometric metallicity. Applying this model to XP spectra, we found that this model enables the identification of binaries with mass ratios between 0.5 and 1.0 and flux ratios larger than 0.1 as “poor” fits to the data, either in spectral shape or flux normalization. From an initial sample of 35 million stars within 1 kpc, we identified 14 million possible binary candidates, and a high-confidence “golden sample” of 1 million binary systems. This large homogeneous sample of spectrophotometry-based binaries enables population studies of luminous MSMS binaries and – in conjunction with kinematic or astrometric probes – permits identification of binaries with dark or dim companions, such as white dwarfs, neutron star and black hole candidates, improving our understanding of compact object populations.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-11-05
DOI
https://doi.org/10.1051/0004-6361/202556362
Citations
2
Primary Topic
Astronomy and Astrophysical Research
Type
article
Field-Weighted Citation Impact
2.38
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article

Millions of main-sequence binary stars from Gaia BP/RP spectra

Rhys Seeburger, Hans‐Walter Rix, Kareem J. El-Badry, Gregory Green et al.
2 citations
Astronomy and Astrophysics
Astronomy and Astrophysical Research
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article

Millions of main-sequence binary stars from Gaia BP/RP spectra

Rhys Seeburger, Hans‐Walter Rix, Kareem J. El-Badry, Gregory Green, Johanna Müller-Horn, Jiadong Li, Yuan-Sen Ting, Xiangyu Zhang, Chao Liu
article en
2 citations

Abstract

We present an extensive catalog of likely main-sequence main-sequence (MSMS) binary stars systems derived from Gaia Data Release 3 BP/RP (XP) spectra through the comparison of single- and binary-star model fits. Leveraging the large sample of low-resolution Gaia XP spectra, we used a neural network to build a forward model for spectral luminosities of single stars as a function of stellar mass and photometric metallicity. Applying this model to XP spectra, we found that this model enables the identification of binaries with mass ratios between 0.5 and 1.0 and flux ratios larger than 0.1 as “poor” fits to the data, either in spectral shape or flux normalization. From an initial sample of 35 million stars within 1 kpc, we identified 14 million possible binary candidates, and a high-confidence “golden sample” of 1 million binary systems. This large homogeneous sample of spectrophotometry-based binaries enables population studies of luminous MSMS binaries and – in conjunction with kinematic or astrometric probes – permits identification of binaries with dark or dim companions, such as white dwarfs, neutron star and black hole candidates, improving our understanding of compact object populations.

Astronomy and AstrophysicsVol. 704
California Institute of Technology (US), Laboratoire AstroParticule et Cosmologie (FR), Max Planck Institute for Astronomy (DE), Zhejiang Lab (CN), Astronomy and Space (AU), National Astronomical Observatories (CN), University of Chinese Academy of Sciences (CN), The Ohio State University (US), Netherlands Institute for Radio Astronomy (NL)
Openalex Percentile: Top 10%
Astronomy and Astrophysical Research
2.38
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