The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered ...throughout the galaxy, solely based on their chemistry. This concept, known as strong chemical tagging is a major objective of spectroscopic studies, but it has yet to yield the anticipated results. We assess the existence and the robustness of the relation between chemical abundances and the birthplace using known member stars of open clusters. We followed a supervised machine learning approach, using chemical abundances obtained from APOGEE DR17, observed open clusters as labels, and different data preprocessing techniques. We find that open clusters can be recovered with any classifier and on data whose features are not carefully selected. In the sample with no field stars, we obtain an average accuracy of $75.2<!PCT!>$ and we find that the prediction accuracy mostly depends on the uncertainties of the chemical abundances. When field stars outnumber the cluster members, the performance degrades. Our results show the difficulty of recovering birth clusters using chemistry alone, even in a supervised scenario. This clearly challenges the feasibility of strong chemical tagging. Nevertheless, including information about ages could potentially enhance the possibility of recovering birth clusters.
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 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.
The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered ...throughout the galaxy, solely based on their chemistry. This concept, known as "strong chemical tagging", is a major objective of spectroscopic studies, but has yet to yield the anticipated results. We assess the existence and the robustness of the relation between chemical abundances and birth place using known member stars of open clusters. We followed a supervised machine learning approach, using chemical abundances obtained from APOGEE DR17, observed open clusters as labels and different data preprocessing techniques. We found that open clusters can be recovered with any classifier and on data whose features are not carefully selected. In the sample with no field stars, we obtain an average accuracy of \(75.2\%\) and we find that the prediction accuracy depends mostly on the uncertainties of the chemical abundances. When field stars outnumber the cluster members, the performance degrades. Our results show the difficulty of recovering birth clusters using chemistry alone, even in a supervised scenario. This clearly challenges the feasibility of strong chemical tagging. Nevertheless, including information about ages could potentially enhance the possibility of recovering birth clusters.
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.
