In 2022 June, the Gaia mission released a catalog of astrometric orbital solutions for 168,065 binary systems, by far the largest such catalog to date. The catalog’s selection function is difficult to characterize because of choices made in its construction. Understanding the catalog’s selection function is required to model and interpret its contents. We use a combination of analytic and empirical prescriptions to construct a function that computes the probability that a binary with a given set of properties would have been published in the Gaia Data Release 3 astrometric orbit catalog. This is a complementary approach to the more accurate but significantly more computationally expensive approach of El-Badry et al. We also construct a binary population synthesis model to validate our characterization of the selection function, finding good agreement with the actual Gaia NSS catalog, with the exception of the orbital eccentricity distribution. The NSS catalog suggests high-eccentricity orbits are relatively uncommon at intermediate periods 100 ≲ Porb ≲ 1000 days. As an example application of the selection function, we estimate the Gaia DR3 detection probabilities of the star + BH binaries Gaia BH1 and BH2, and find them to be 0.38 and 0.27, respectively. Compared to the values obtained by detailed modeling in El-Badry et al., the probabilities are identical for BH1, and within a factor of 2 for BH2. We also estimate the population of Sun-like star + BH binaries in the Galaxy to be ∼3000 for 100 < Porb < 400 days, <800 for 400 < Porb < 1000 days, and <12,000 for 1000 < Porb < 1500 days.
A Fast, Analytic Empirical Model of the Gaia Data Release 3 Astrometric Orbit Catalog Selection Function
Abstract
Copyright and License
© 2025. The Author(s). Published by the American Astronomical Society.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
We thank the referee for comments on the manuscript. C.Y.L. acknowledges support from the Harrison and Carnegie Fellowships. K.E.-B. is supported by NSF grant AST-2307232. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work has made use of NASA’s Astrophysics Data System.
Facilities
Gaia. -
Software References
isochrones (T. D. Morton 2015), galaxia (S. Sharma et al. 2011), astropy (Astropy Collaboration et al. 2013, 2018, 2022), Matplotlib (J. D. Hunter 2007), NumPy (S. van der Walt et al. 2011), SciPy (P. Virtanen et al. 2020), healpy (A. Zonca et al. 2019), mwdust (J. Bovy et al. 2019), COSMIC (K. Breivik et al. 2020).
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Additional details
- Carnegie Institution for Science
- National Science Foundation
- AST-2307232
- Accepted
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2025-05-14
- Available
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2025-07-09Published
- Caltech groups
- Astronomy Department, Division of Physics, Mathematics and Astronomy (PMA)
- Publication Status
- Published