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; Tomaž Zwitter

doi:10.3847/1538-4357/ac5fa7

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. MartellKatharine J. Schlesinger, Sanjib Sharma, Tomaž Zwitter

^*Corresponding author for this work

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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.

Original language	English
Article number	47
Pages (from-to)	1-21
Number of pages	21
Journal	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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© 2022. The Author(s). Published by the American Astronomical Society. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Publisher version (open access)Final published version, 11.5 MBLicence: CC BY

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Hughes, A. C. N., Spitler, L. R., Zucker, D. B., Nordlander, T., Simpson, J., Da Costa, G. S., Ting, Y.-S., Li, C., Bland-Hawthorn, J., Buder, S., Casey, A. R., De Silva, G. M., D'Orazi, V., Freeman, K. C., Hayden, M. R., Kos, J., Lewis, G. F., Lin, J., Lind, K., ... Zwitter, T. (2022). The GALAH Survey: A new sample of extremely metal-poor stars using a machine-learning classification algorithm. Astrophysical Journal, 930(1), 1-21. Article 47. https://doi.org/10.3847/1538-4357/ac5fa7

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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 Yuan-Sen Ting 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 Toma{\v z} Zwitter",

note = "{\textcopyright} 2022. The Author(s). Published by the American Astronomical Society. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",

year = "2022",

month = may,

day = "1",

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

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Hughes, ACN, Spitler, LR , Zucker, DB, Nordlander, T, Simpson, J, Da Costa, GS, Ting, Y-S, Li, C, Bland-Hawthorn, J, Buder, S, Casey, AR, De Silva, GM, D'Orazi, V, Freeman, KC, Hayden, MR, Kos, J, Lewis, GF, Lin, J, Lind, K, Martell, SL, Schlesinger, KJ, Sharma, S & Zwitter, T 2022, 'The GALAH Survey: A new sample of extremely metal-poor stars using a machine-learning classification algorithm', Astrophysical Journal, vol. 930, no. 1, 47, pp. 1-21. https://doi.org/10.3847/1538-4357/ac5fa7

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, Tomaž

N1 - © 2022. The Author(s). Published by the American Astronomical Society. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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.

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U2 - 10.3847/1538-4357/ac5fa7

DO - 10.3847/1538-4357/ac5fa7

M3 - Article

AN - SCOPUS:85130181500

SN - 0004-637X

VL - 930

SP - 1

EP - 21

JO - Astrophysical Journal

JF - 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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