# Identifying Stellar Streams in Gaia DR2 with Data Mining Techniques

## TL;DR

This paper uses data mining on Gaia DR2 data to identify stellar streams by analyzing integrals of motion, discovering seven streams including four new ones, and linking them to the Gaia-Enceladus progenitor.

## Contribution

It introduces a novel combination of data mining, numerical, and statistical techniques to detect stellar streams in large astronomical datasets, including previously unknown streams.

## Key findings

- Seven stellar streams identified, four new discoveries.
- Streams are old, metal-poor, and tightly clustered in integral of motion space.
- New streams are likely fragments of Gaia-Enceladus progenitor.

## Abstract

Streams of stars from captured dwarf galaxies and dissolved globular clusters are identifiable through the similarity of their orbital parameters, a fact that remains true long after the streams have dispersed spatially. We calculate the integrals of motion for 44855 stars, to a distance of 4 kpc from the Sun, which have full and accurate 6D phase space positions in the Gaia DR2 catalogue. We then apply a novel combination of data mining, numerical and statistical techniques to search for stellar streams. This process returns seven high-confidence streams (including four that were not previously known), all of which display tight clustering in the integral of motion space. Colour-magnitude diagrams indicate that these streams are relatively simple, old, metal-poor populations. A combined evaluation of the kinematics and colour-magnitude properties suggests that the previously undiscovered streams are fragments of the Gaia-Enceladus progenitor. The success of this project demonstrates the usefulness of data mining techniques in exploring large datasets.