The GALAH Survey: A New Sample of Extremely Metal-poor Stars Using a Machine-learning Classification Algorithm

Abstract

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 ∼600,000 high-resolution stellar spectra from the GALAH survey plus a machine-learning algorithm to find 54 candidates with estimated [Fe/H] ≤−3.0, six of which have [Fe/H] ≤−3.5. Our sample includes ∼20% main-sequence EMP candidates, unusually high for EMP star 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.