Abstract
Stellar chemical abundances have proved themselves a key source of
information for understanding the evolution of the Milky Way, and the scale of
major stellar surveys such as GALAH have massively increased the amount of
chemical data available. However, progress is hampered by the level of
precision in chemical abundance data as well as the visualization methods for
comparing the multidimensional outputs of chemical evolution models to stellar
abundance data. Machine learning methods have greatly improved the former;
while the application of tree-building or phylogenetic methods borrowed from
biology are beginning to show promise with the latter. Here we analyse a sample
of GALAH solar twins to address these issues. We apply The Cannon algorithm
(Ness et al. (2015)) to generate a catalogue of about 40,000 solar twins with
14 high precision abundances which we use to perform a phylogenetic analysis on
a selection of stars that have two different ranges of eccentricities. From our
analyses we are able to find a group with mostly stars on circular orbits and
some old stars with eccentric orbits whose age-[Y/Mg] relation agrees
remarkably well with the chemical clocks published by previous high precision
abundance studies. Our results show the power of combining survey data with
machine learning and phylogenetics to reconstruct the history of the Milky Way.