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Stellar chemical abundances have proved themselves a key source ofinformation for understanding the evolution of the Milky Way, and the scale ofmajor stellar surveys such as GALAH have massively increased the amount ofchemical data available. However, progress is hampered by the level ofprecision in chemical abundance data as well as the visualization methods forcomparing the multidimensional outputs of chemical evolution models to stellarabundance data. Machine learning methods have greatly improved the former;while the application of tree-building or phylogenetic methods borrowed frombiology are beginning to show promise with the latter. Here we analyse a sampleof GALAH solar twins to address these issues. We apply The Cannon algorithm togenerate a catalogue of about 40,000 solar twins with 14 high precisionabundances which we use to perform a phylogenetic analysis on a selection ofstars that have two different ranges of eccentricities. From our analyses weare able to find a group with mostly stars on circular orbits and some oldstars with eccentric orbits whose age-[Y/Mg] relation agrees remarkably wellwith the chemical clocks published by previous high precision abundancestudies. Our results show the power of combining survey data with machinelearning and phylogenetics to reconstruct the history of the Milky Way.

Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies