The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm
Abstract
Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical enrichment in the Milky Way. Here we leverage a large sample of high-resolution stellar spectra from the GALAH survey plus a machine learning algorithm to find 54 candidates with estimated [Fe/H]~~-3.0, 6 of which have [Fe/H]~~-3.5. Our sample includes main sequence EMP candidates, unusually high for \emp surveys. We find the magnitude-limited metallicity distribution function of our sample is consistent with previous work that used more complex selection criteria. The method we present has significant potential for application to the next generation of massive stellar spectroscopic surveys, which will expand the available spectroscopic data well into the millions of stars.
Keywords
Cite
@article{arxiv.2203.10843,
title = {The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm},
author = {Arvind C. N. Hughes and Lee R. Spitler and Daniel B. Zucker and Thomas Nordlander and Jeffrey Simpson and Gary S. Da Costa and Yuan-Sen Ting and Chengyuan Li and Joss Bland-Hawthorn and Sven Buder and Andrew R. Casey and Gayandhi M. De Silva and Valentina D'Orazi and Ken C. Freeman and Michael R. Hayden and Janez Kos and Geraint F. Lewis and Jane Lin and Karin Lind and Sarah L. Martell and Katharine J. Schlesinger and Sanjib Sharma and Tomaz Zwitter and The GALAH Collaboration},
journal= {arXiv preprint arXiv:2203.10843},
year = {2022}
}
Comments
27 pages, 20 figures, accepted for publication in ApJ, candidate table available at this https://github.com/arvhug/GALAH---TSNE_EMP