Inferring binary and trinary stellar populations in photometric and astrometric surveys

TL;DR

This paper develops a Bayesian hierarchical model to classify and analyze unresolved binary and trinary stellar systems in Gaia and 2MASS data, enhancing understanding of stellar populations in the Milky Way.

Contribution

It introduces a data-driven method for jointly fitting photometric and astrometric data to identify multiple stellar systems without external models.

Findings

Successfully classifies single, binary, and trinary systems.

Predicts properties of unresolved stars in multiple systems.

Prepares for validation with Gaia Data Release 4.

Abstract

Multiple stellar systems are ubiquitous in the Milky Way, but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. Yet, modeling them is essential for developing a full understanding of large surveys such as Gaia, and connecting them to stellar and Galactic models. In this paper we address this problem by jointly fitting the Gaia and 2MASS photometric and astrometric data using a data-driven Bayesian hierarchical model that includes populations of binary and trinary systems. This allows us to classify observations into singles, binaries, and trinaries, in a robust and efficient manner, without resorting to external models. We are able to identify multiple systems and, in some cases, make strong predictions for the properties of its unresolved stars. We will be able to compare such predictions with Gaia Data Release 4, which will contain astrometric identification and analysis of binary systems.