StarGO: A New Method to Identify the Galactic Origins of Halo Stars

TL;DR

StarGO is a novel unsupervised learning method using self-organizing maps to identify the galactic origins of halo stars, outperforming traditional methods in simulated data and promising detailed insights into the Milky Way's stellar halo.

Contribution

We introduce StarGO, a new parameter-free method combining SOM with adaptive grouping to trace the origins of halo stars, validated on synthetic Milky Way halo data.

Findings

StarGO identifies more satellite origins than FoF method.

StarGO achieves higher star identification fractions.

Method performs well on Gaia-like observational uncertainties.

Abstract

We develop a new method StarGO (Stars' Galactic Origin) to identify the galactic origins of halo stars using their kinematics. Our method is based on self-organizing map (SOM), which is one of the most popular unsupervised learning algorithms. StarGO combines SOM with a novel adaptive group identification algorithm with essentially no free parameters. In order to evaluate our model, we build a synthetic stellar halo from mergers of nine satellites in the Milky Way. We construct the mock catalogue by extracting a heliocentric volume of 10 kpc from our simulations and assigning expected observational uncertainties corresponding to bright stars from Gaia DR2 and LAMOST DR5. We compare the results from StarGO against that from a Friends-of-Friends (FoF) based method in the space of orbital energy and angular momentum. We show that StarGO is able to systematically identify more satellites and achieve higher number fraction of identified stars for most of the satellites within the extracted volume. When applied to data from Gaia DR2, StarGO will enable us to reveal the origins of the inner stellar halo in unprecedented detail.