2 research outputs found
Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies
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 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.Comment: Accepted in MNRAS journal. Associated catalog of high precision,
Cannon-rederived abundances for GALAH solar twins to be made publicly
available upon publication and available now upon request. See Manea et al.
2023 for a complementary, high precision, Cannon-rederived abundance catalog
for GALAH red giant star
Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies
© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/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 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.Peer reviewe