Chemo-kinematic analysis of metal-poor stars with unsupervised machine learning (2012.03949v1)

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.