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arXiv · 2404.14127

Gaia DR3 detectability of unresolved binary systems

Abstract

Gaia can not individually resolve very close binary systems, however, the collected data can still be used to identify them. A powerful indicator of stellar multiplicity is the sources reported Renormalized Unit Weight Error (ruwe), which effectively captures the astrometric deviations from single-source solutions. We aim to characterise the imprints left on ruwe caused by binarity. By flagging potential binary systems based on ruwe, we aim to characterise which of their properties will contribute the most to their detectability. We develop a model to estimate ruwe values for observations of Gaia sources, based on the biases to the single-source astrometric track arising from the presence of an unseen companion. Then, using the recipes from previous GaiaUnlimited selection functions, we estimate the selection probability of sources with high ruwe, and discuss what binary properties contribute to increasing the sources ruwe. We compute the maximum ruwe value which is compatible with single-source solutions as a function of their location on-sky. We see that binary systems selected as sources with a ruwe higher than this sky-varying threshold have a strong detectability window in their orbital period distribution, which peaks at periods equal to the Gaia observation time baseline. We demonstrate how our sky-varying ruwe threshold provides a more complete sample of binary systems when compared to single sky-averaged values by studying the unresolved binary population in the Gaia Catalogue of Nearby Stars. We provide the code and tools used in this study, as well as the sky-varying ruwe threshold through the GaiaUnlimited Python package

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Alfred Castro-Ginard, Zephyr Penoyre, Andrew R. Casey, Anthony G. A. Brown, Vasily Belokurov, Tristan Cantat-Gaudin, Ronald Drimmel, Morgan Fouesneau, Shourya Khanna, Evgeny P. Kurbatov, Adrian M. Price-Whelan, Hans-Walter Rix, Richard L. Smart. 2024-04-22. Gaia DR3 detectability of unresolved binary systems. https://doi.org/10.1051/0004-6361%2F202450172

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