Cataloging accreted stars within Gaia DR2 using deep learning

¹ Institute of Theoretical Science, Department of Physics, University of Oregon, Eugene, OR 97403, USA
e-mail: bostdiek@g.harvard.edu
² Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA 91125, USA
e-mail: lnecib@caltech.edu
³ Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel
⁴ School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540, USA
⁵ Department of Physics, Princeton University, Princeton, NJ 08544, USA
⁶ TAPIR, California Institute of Technology, Pasadena, CA 91125, USA
⁷ Department of Physics, University of California, Davis, CA 95616, USA
⁸ Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
⁹ Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA

Received: 7 October 2019
Accepted: 21 January 2020

Abstract

Aims. The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2.

Methods. A method known as “transfer learning” is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way.

Results. The result is a catalog that identifies ∼767 000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.