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

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, Tomaz Zwitter

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

9 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
Journal	Astrophysical Journal
Volume	930
Issue number	1
DOIs	https://doi.org/10.3847/1538-4357/ac5fa7
Publication status	Published - 1 May 2022

Access to Document

10.3847/1538-4357/ac5fa7

Cite this

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), Article 47. https://doi.org/10.3847/1538-4357/ac5fa7

@article{9ada1b163bb8410c8dddbbe47f6e9d9e,

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",

note = "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 = "Astrophysical Journal",

issn = "0004-637X",

publisher = "American Astronomical Society",

number = "1",

}

Hughes, ACN, Spitler, LR, Zucker, DB, Nordlander, T, Simpson, J, Da Costa, GS, Ting, YS, 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. 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, Tomaz

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 - http://www.scopus.com/inward/record.url?scp=85130181500&partnerID=8YFLogxK

U2 - 10.3847/1538-4357/ac5fa7

DO - 10.3847/1538-4357/ac5fa7

M3 - Article

SN - 0004-637X

VL - 930

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

Abstract

Access to Document

Other files and links

Fingerprint

Cite this