Papers › The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning...
The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm
Arvind C. N. Hughes, Lee R. Spitler, Daniel B. Zucker, Thomas Nordlander, Jeffrey Simpson, Gary S. Da Costa, Yuan-Sen Ting, Chengyuan Li, Joss Bland-Hawthorn, Sven Buder, Andrew R. Casey, Gayandhi M. De Silva, Valentina D'Orazi, Ken C. Freeman, Michael R. Hayden, Janez Kos, Geraint F. Lewis, Jane Lin, Karin Lind, Sarah L. Martell, Katharine J. Schlesinger, Sanjib Sharma, Tomaz Zwitter, The GALAH Collaboration
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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, 6 of which have [Fe/H]~≤~-3.5. Our sample includes ∼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.
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