Inferring binary and trinary stellar populations in photometric and
  astrometric surveys
  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.
