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