The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A
  Machine Learning Classification Algorithm
  Extremely Metal-Poor (EMP) stars provide a valuable probe of early chemical
enrichment in the Milky Way. Here we leverage a large sample of $\sim600,000$
high-resolution stellar spectra from the GALAH survey plus a machine learning
algorithm to find 54 candidates with estimated [Fe/H]~$\leq$~-3.0, 6 of which
have [Fe/H]~$\leq$~-3.5. Our sample includes $\sim 20 \%$ 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.
