  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. 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. The result is a catalog that
identifies around 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.
