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- W3097943980 abstract "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." @default.
- W3097943980 created "2020-11-09" @default.
- W3097943980 creator A5007260627 @default.
- W3097943980 creator A5012311980 @default.
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- W3097943980 creator A5076033932 @default.
- W3097943980 creator A5085184029 @default.
- W3097943980 date "2020-10-29" @default.
- W3097943980 modified "2023-10-05" @default.
- W3097943980 title "Chasing Accreted Structures within Gaia DR2 Using Deep Learning" @default.
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