Chemo-kinematic analysis of metal-poor stars with unsupervised machine learning

TL;DR

This paper uses unsupervised machine learning to analyze the chemo-kinematic properties of metal-poor stars, aiming to identify stellar populations and merger remnants in the Galaxy.

Contribution

It introduces a chemo-kinematic analysis method applying hierarchical clustering and k-means to metal-poor stars from the GALAH survey, focusing on identifying Galactic stellar groups.

Findings

Preliminary identification of stellar groups related to merger events.

Application of unsupervised learning to chemo-kinematic data.

Potential to improve separation of Galactic stellar populations.

Abstract

Metal-poor stars play an import role in the understanding of Galaxy formation and evolution. Evidence of the early mergers that built up the Galaxy might remain in the distributions of abundances, kinematics, and orbital parameters of the stars. In this work, we report on preliminary results of an on-going chemo-kinematic analysis of a sample of metal-poor ([Fe/H] $\leq$ -1.0) stars observed by the GALAH spectroscopic survey. We explored the chemical and orbital data with unsupervised machine learning (hierarchical clustering, k-means cluster analysis and correlation matrices). Our final goal is to find an optimal way to separate different Galactic stellar populations and stellar groups originating from merging events, such as Gaia-Enceladus and Sequoia.