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

the GALAH collaboration

doi:10.3847/1538-4357/ac5fa7

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

the GALAH collaboration

School of Physics and Astronomy

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15 Citations (Scopus)

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.

Original language	English
Article number	47
Number of pages	21
Journal	The Astrophysical Journal
Volume	930
Issue number	1
DOIs	https://doi.org/10.3847/1538-4357/ac5fa7
Publication status	Published - 1 May 2022

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10.3847/1538-4357/ac5fa7Licence: CC BY

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@article{3fdf47160ef1425daab5ec16264f0a74,

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

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. ",

author = "Hughes, \{Arvind C.N.\} and Spitler, \{Lee R.\} and Zucker, \{Daniel B.\} and Thomas Nordlander and Jeffrey Simpson and \{Da Costa\}, \{Gary S.\} and Ting, \{Yuan Sen\} and Chengyuan Li and Joss Bland-Hawthorn and Sven Buder and Casey, \{Andrew R.\} and \{De Silva\}, \{Gayandhi M.\} and Valentina D'Orazi and Freeman, \{Ken C.\} and Hayden, \{Michael R.\} and Janez Kos and Lewis, \{Geraint F.\} and Jane Lin and Karin Lind and Martell, \{Sarah L.\} and Schlesinger, \{Katharine J.\} and Sanjib Sharma and Tomaz Zwitter and \{the GALAH collaboration\}",

note = "Funding Information: We are grateful to the anonymous referee for their helpful comments and suggestions. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. L.S. acknowledges support from Australian Research Council Discovery Project DP190102448. D.B.Z., J.S., and S.L.M. acknowledge support from Australian Research Council Discovery Project DP180101791. Y.S.T. acknowledges support from the Australian Research Council through DECRA Fellowship DE220101520. This work was based on data acquired at the Anglo-Australian Telescope. We acknowledge the traditional custodians of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. Publisher Copyright: {\textcopyright} 2022. The Author(s). Published by the American Astronomical Society.",

year = "2022",

month = may,

day = "1",

doi = "10.3847/1538-4357/ac5fa7",

language = "English",

volume = "930",

journal = "The Astrophysical Journal",

issn = "1538-4357",

publisher = "IOP Publishing",

number = "1",

}

TY - JOUR

T1 - The GALAH Survey

T2 - A New Sample of Extremely Metal-poor Stars Using a Machine-learning Classification Algorithm

AU - Hughes, Arvind C.N.

AU - Spitler, Lee R.

AU - Zucker, Daniel B.

AU - Nordlander, Thomas

AU - Simpson, Jeffrey

AU - Da Costa, Gary S.

AU - Ting, Yuan Sen

AU - Li, Chengyuan

AU - Bland-Hawthorn, Joss

AU - Buder, Sven

AU - Casey, Andrew R.

AU - De Silva, Gayandhi M.

AU - D'Orazi, Valentina

AU - Freeman, Ken C.

AU - Hayden, Michael R.

AU - Kos, Janez

AU - Lewis, Geraint F.

AU - Lin, Jane

AU - Lind, Karin

AU - Martell, Sarah L.

AU - Schlesinger, Katharine J.

AU - Sharma, Sanjib

AU - Zwitter, Tomaz

AU - the GALAH collaboration

N1 - Funding Information: We are grateful to the anonymous referee for their helpful comments and suggestions. Parts of this research were supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. L.S. acknowledges support from Australian Research Council Discovery Project DP190102448. D.B.Z., J.S., and S.L.M. acknowledge support from Australian Research Council Discovery Project DP180101791. Y.S.T. acknowledges support from the Australian Research Council through DECRA Fellowship DE220101520. This work was based on data acquired at the Anglo-Australian Telescope. We acknowledge the traditional custodians of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. Publisher Copyright: © 2022. The Author(s). Published by the American Astronomical Society.

PY - 2022/5/1

Y1 - 2022/5/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85130181500

U2 - 10.3847/1538-4357/ac5fa7

DO - 10.3847/1538-4357/ac5fa7

M3 - Article

AN - SCOPUS:85130181500

SN - 1538-4357

VL - 930

JO - The Astrophysical Journal

JF - The Astrophysical Journal

IS - 1

M1 - 47

ER -

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

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ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions

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

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ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions

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