Chasing Accreted Structures within Gaia DR2 Using Deep Learning
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 $r\in [6.5,9.5]\,{\rm{kpc}}$ and vertical distances $| z| \lt 3\,{\rm{kpc}}$ . 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 200 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.
- Publication:
-
The Astrophysical Journal
- Pub Date:
- November 2020
- DOI:
- 10.3847/1538-4357/abb814
- arXiv:
- arXiv:1907.07681
- Bibcode:
- 2020ApJ...903...25N
- Keywords:
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- Milky Way dynamics;
- Galaxy dynamics;
- Astrometry;
- Neural networks;
- Star clusters;
- 1051;
- 591;
- 80;
- 1933;
- 1567;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Cosmology and Nongalactic Astrophysics
- E-Print:
- 15 + 13 pages, 9 + 15 figures. v2: Minor changes in calculating probabilities to belong to a kinematic structure. Conclusions unchanged