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
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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.
AbstractList 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.
Author Li, Chengyuan
Buder, Sven
Da Costa, Gary S.
Bland-Hawthorn, Joss
Casey, Andrew R.
De Silva, Gayandhi M.
Lin, Jane
Freeman, Ken C.
Lind, Karin
Spitler, Lee R.
Kos, Janez
Zwitter, Tomaž
Ting, Yuan-Sen
Martell, Sarah L.
Hayden, Michael R.
Simpson, Jeffrey
D’Orazi, Valentina
Schlesinger, Katharine J.
Sharma, Sanjib
Nordlander, Thomas
Hughes, Arvind C. N.
Zucker, Daniel B.
Lewis, Geraint F.
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  organization: Macquarie University Australian Astronomical Optics, Faculty of Science and Engineering, Macquarie Park, NSW 2113, Australia
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  givenname: Gayandhi M.
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  surname: D’Orazi
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  organization: Istituto Nazionale di Astrofisica , Osservatorio Astronomico di Padova, vicolo dell’Osservatorio 5, I-35122, Padova, Italy
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  givenname: Ken C.
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  surname: Lind
  fullname: Lind, Karin
  organization: Stockholm University Department of Astronomy, AlbaNova University Centre, SE-106 91 Stockholm, Sweden
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  givenname: Sarah L.
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  surname: Martell
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Snippet 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...
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StartPage 47
SubjectTerms 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
Title The GALAH Survey: A New Sample of Extremely Metal-poor Stars Using a Machine-learning Classification Algorithm
URI https://iopscience.iop.org/article/10.3847/1538-4357/ac5fa7
https://www.proquest.com/docview/2659695965
https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-205005
Volume 930
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