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Published September 2021 | public
Journal Article

A million binaries from Gaia eDR3: sample selection and validation of Gaia parallax uncertainties

Abstract

We construct from Gaia eDR3 an extensive catalogue of spatially resolved binary stars within ≈1 kpc of the Sun, with projected separations ranging from a few au to 1 pc. We estimate the probability that each pair is a chance alignment empirically, using the Gaia catalogue itself to calculate the rate of chance alignments as a function of observables. The catalogue contains 1.3 (1.1) million binaries with >90 per cent (>99 per cent) probability of being bound, including 16 000 white dwarf – main-sequence (WD + MS) binaries and 1400 WD + WD binaries. We make the full catalogue publicly available, as well as the queries and code to produce it. We then use this sample to calibrate the published Gaia DR3 parallax uncertainties, making use of the binary components' near-identical parallaxes. We show that these uncertainties are generally reliable for faint stars (G ≳ 18), but are underestimated significantly for brighter stars. The underestimates are generally $\leq30{{\ \rm per\ cent}}$ for isolated sources with well-behaved astrometry, but are larger (up to ∼80 per cent) for apparently well-behaved sources with a companion within ≲4 arcsec, and much larger for sources with poor astrometric fits. We provide an empirical fitting function to inflate published σϖ values for isolated sources. The public catalogue offers wide ranging follow-up opportunities: from calibrating spectroscopic surveys, to precisely constraining ages of field stars, to the masses and the initial–final mass relation of WDs, to dynamically probing the Galactic tidal field.

Additional Information

© 2021 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). We thank the anonymous referee for a constructive report, Jackie Faherty for help creating visualizations of the catalogue, and Eliot Quataert, Dan Weisz, Jan Rybizki, and Anthony Brown for helpful comments. We acknowledge earlier discussions with Tim Brandt that proved seminal for this paper. We are grateful to Geoff Tabin and In-Hei Hahn for their continued hospitality during the writing of this manuscript. KE was supported by an NSF graduate research fellowship and a Hellman fellowship from UC Berkeley. TMH acknowledges support from the National Science Foundation under Grant No. AST-1908119. This project was developed in part during the 2020 virtual eDR3 Unboxing Gaia Sprint. 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. Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope LAMOST) is a National Major Scientific Project built by the Chinese Academy of Sciences. Funding for the project has been provided by the National Development and Reform Commission. LAMOST is operated and managed by the National Astronomical Observatories, Chinese Academy of Sciences. This research made use of Astropy,5 a community-developed core python package for Astronomy (Astropy Collaboration 2013, 2018). This research made use of the cross-match service provided by CDS, Strasbourg. DATA AVAILABILITY. All the data used in this paper is publicly available. The Gaia data can be retrieved through the Gaia archive (https://gea.esac.esa.int/archive), and the LAMOST data are available at http://dr6.lamost.org. The binary catalogueand code to produce it can be found at https://zenodo.org/record/4435257.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023