Chasing Accreted Structures within Gaia DR2 using Deep Learning. (arXiv:1907.07681v1 [astro-ph.GA])

Chasing Accreted Structures within Gaia DR2 using Deep Learning. (arXiv:1907.07681v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Necib_L/0/1/0/all/0/1">Lina Necib</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ostdiek_B/0/1/0/all/0/1">Bryan Ostdiek</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lisanti_M/0/1/0/all/0/1">Mariangela Lisanti</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cohen_T/0/1/0/all/0/1">Timothy Cohen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Freytsis_M/0/1/0/all/0/1">Marat Freytsis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Garrison_Kimmel_S/0/1/0/all/0/1">Shea Garrison-Kimmel</a>

In Ostdiek et al. (2019), 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, first introduced in Necib et al. (2019a), is a vast stream consisting of at least 500 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.

In Ostdiek et al. (2019), 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, first
introduced in Necib et al. (2019a), is a vast stream consisting of at least 500
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.

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