# Chasing Accreted Structures within Gaia DR2 using Deep Learning

## TL;DR

This paper uses deep learning and clustering algorithms on Gaia DR2 data to identify known and new stellar streams, revealing insights into the Milky Way's merger history.

## Contribution

It introduces a novel combination of deep neural networks and clustering methods to detect and analyze accreted stellar structures in Gaia data.

## Key findings

- Identification of known structures like Gaia Enceladus and Helmi stream.
- Discovery of a new large stream called Nyx with at least 90 stars.
- Demonstration of machine learning effectiveness in galactic archaeology.

## 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.