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 ∼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...

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Veröffentlicht in:	The Astrophysical journal Jg. 930; H. 1; S. 47 - 67
Hauptverfasser:	Hughes, Arvind C. N., Spitler, Lee R., Zucker, Daniel B., Nordlander, Thomas, Simpson, Jeffrey, Da Costa, Gary S., Ting, Yuan-Sen, Li, Chengyuan, Bland-Hawthorn, Joss, Buder, Sven, Casey, Andrew R., De Silva, Gayandhi M., D’Orazi, Valentina, Freeman, Ken C., Hayden, Michael R., Kos, Janez, Lewis, Geraint F., Lin, Jane, Lind, Karin, Martell, Sarah L., Schlesinger, Katharine J., Sharma, Sanjib, Zwitter, Tomaž
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia The American Astronomical Society 01.05.2022 IOP Publishing
Schlagworte:	Algorithms Astrophysics CEMP stars Classification Distribution functions Galactic archaeology Galaxy stellar content High resolution spectroscopy Machine learning Metallicity Milky Way Polls & surveys Population II stars Population III stars Spectroscopy Stars Stellar abundances Stellar classification Stellar spectra
ISSN:	0004-637X, 1538-4357, 1538-4357
Online-Zugang:	Volltext
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