Chasing Accreted Structures within Gaia DR2 using Deep Learning

Understand

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.