Possibilities of Identifying Members from Milky Way Satellite Galaxies using Unsupervised Machine Learning Algorithms

TL;DR

This study applies unsupervised density-based clustering algorithms to Gaia and survey data to identify members of Milky Way satellite galaxies, achieving high recovery of known members and discovering new ones.

Contribution

It demonstrates the effectiveness of DBSCAN and HDBSCAN algorithms in identifying satellite galaxy members using astrometric and photometric data.

Findings

Recovered over 80% of known members in most satellites

Rejected 95-100% of non-members

Identified many new potential members

Abstract

A detailed study of stellar populations in Milky Way (MW) satellite galaxies remains an observational challenge due to their faintness and fewer spectroscopically confirmed member stars. We use unsupervised machine learning methods to identify new members for nine nearby MW satellite galaxies using Gaia data release-3 (Gaia DR3) astrometry and the Dark Energy Survey (DES) and the DECam Local Volume Exploration Survey (DELVE) photometry. Two density-based clustering algorithms, DBSCAN and HDBSCAN, have been used in the four-dimensional astrometric parameter space to identify member stars belonging to MW satellite galaxies. Our results indicate that we can recover more than 80% of the known spectroscopically confirmed members in most of the satellite galaxies and also reject 95-100% of spectroscopic non-members. We have also added many new members using this method. We compare our results with previous studies that also use photometric and astrometric data and discuss the suitability of density-based clustering methods for MW satellite galaxies