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The goal of this study is to present the development of a machine learningbased approach that utilizes phase space alone to separate the Gaia DR2 starsinto two categories: those accreted onto the Milky Way from those that are insitu. Traditional selection methods that have been used to identify accretedstars typically rely on full 3D velocity, metallicity information, or both,which significantly reduces the number of classifiable stars. The approachadvocated here is applicable to a much larger portion of Gaia DR2. A methodknown as "transfer learning" is shown to be effective through extensive testingon a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-inhydrodynamic simulations of Milky Way-mass galaxies. The machine is firsttrained on simulated data using only 5D kinematics as inputs and is thenfurther trained on a cross-matched Gaia/RAVE data set, which improvessensitivity to properties of the real Milky Way. The result is a catalog thatidentifies around 767,000 accreted stars within Gaia DR2. This catalog canyield empirical insights into the merger history of the Milky Way and could beused to infer properties of the dark matter distribution.

Cataloging Accreted Stars within Gaia DR2 using Deep Learning