Chasing Accreted Structures within Gaia DR2 using Deep Learning
  In previous work, we developed a deep neural network classifier that only
relies on phase-space information to obtain a catalog of accreted stars based
on the second data release of Gaia (DR2). In this paper, we apply two
clustering algorithms to identify velocity substructure within this catalog. We
focus on the subset of stars with line-of-sight velocity measurements that fall
in the range of Galactocentric radii $r \in [6.5, 9.5]$ kpc and vertical
distances $|z| < 3$ kpc. Known structures such as Gaia Enceladus and the Helmi
stream are identified. The largest previously-unknown structure, Nyx, is a vast
stream consisting of at least 90 stars in the region of interest. This study
displays the power of the machine learning approach by not only successfully
identifying known features, but also discovering new kinematic structures that
may shed light on the merger history of the Milky Way.
