A photometric machine-learning method to infer stellar metallicity

Adam A. Miller

doi:10.1007/978-3-319-16313-0_17

A photometric machine-learning method to infer stellar metallicity

Adam A. Miller^*

^*Corresponding author for this work

Physics and Astronomy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Following its formation, a star’s metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectro-scopic surveys are limited to a few×10⁶ targets; photometric surveys, on the other hand, have detected > 10⁹ stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of ∼120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g^′ ≤ 18 mag), with 4500 K ≤ T_eff ≤ 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of ∼0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra.

Original language	English (US)
Title of host publication	Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings
Editors	Wanming Chu, Shinji Kikuchi, Subhash Bhalla
Publisher	Springer Verlag
Pages	231-236
Number of pages	6
ISBN (Electronic)	9783319163123
DOIs	https://doi.org/10.1007/978-3-319-16313-0_17
State	Published - 2015
Event	10th International Workshop on Databases in Networked Information Systems, DNIS 2015 - Aizu-Wakamatsu, Japan Duration: Mar 23 2015 → Mar 25 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8999
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Workshop on Databases in Networked Information Systems, DNIS 2015
Country/Territory	Japan
City	Aizu-Wakamatsu
Period	3/23/15 → 3/25/15

Keywords

Machine learning
Photometric surveys
Random forest
Stellar metallicity

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-16313-0_17

Cite this

Miller, A. A. (2015). A photometric machine-learning method to infer stellar metallicity. In W. Chu, S. Kikuchi, & S. Bhalla (Eds.), Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings (pp. 231-236). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8999). Springer Verlag. https://doi.org/10.1007/978-3-319-16313-0_17

Miller, Adam A. / A photometric machine-learning method to infer stellar metallicity. Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings. editor / Wanming Chu ; Shinji Kikuchi ; Subhash Bhalla. Springer Verlag, 2015. pp. 231-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A photometric machine-learning method to infer stellar metallicity",

abstract = "Following its formation, a star{\textquoteright}s metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectro-scopic surveys are limited to a few×106 targets; photometric surveys, on the other hand, have detected > 109 stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of ∼120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g′ ≤ 18 mag), with 4500 K ≤ Teff ≤ 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of ∼0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra.",

keywords = "Machine learning, Photometric surveys, Random forest, Stellar metallicity",

author = "Miller, \{Adam A.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 10th International Workshop on Databases in Networked Information Systems, DNIS 2015 ; Conference date: 23-03-2015 Through 25-03-2015",

year = "2015",

doi = "10.1007/978-3-319-16313-0\_17",

language = "English (US)",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "231--236",

editor = "Wanming Chu and Shinji Kikuchi and Subhash Bhalla",

booktitle = "Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings",

}

Miller, AA 2015, A photometric machine-learning method to infer stellar metallicity. in W Chu, S Kikuchi & S Bhalla (eds), Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8999, Springer Verlag, pp. 231-236, 10th International Workshop on Databases in Networked Information Systems, DNIS 2015, Aizu-Wakamatsu, Japan, 3/23/15. https://doi.org/10.1007/978-3-319-16313-0_17

A photometric machine-learning method to infer stellar metallicity. / Miller, Adam A.
Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings. ed. / Wanming Chu; Shinji Kikuchi; Subhash Bhalla. Springer Verlag, 2015. p. 231-236 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8999).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A photometric machine-learning method to infer stellar metallicity

AU - Miller, Adam A.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Following its formation, a star’s metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectro-scopic surveys are limited to a few×106 targets; photometric surveys, on the other hand, have detected > 109 stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of ∼120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g′ ≤ 18 mag), with 4500 K ≤ Teff ≤ 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of ∼0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra.

AB - Following its formation, a star’s metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectro-scopic surveys are limited to a few×106 targets; photometric surveys, on the other hand, have detected > 109 stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of ∼120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g′ ≤ 18 mag), with 4500 K ≤ Teff ≤ 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of ∼0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra.

KW - Machine learning

KW - Photometric surveys

KW - Random forest

KW - Stellar metallicity

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U2 - 10.1007/978-3-319-16313-0_17

DO - 10.1007/978-3-319-16313-0_17

M3 - Conference contribution

AN - SCOPUS:84924627695

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 231

EP - 236

BT - Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings

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A2 - Kikuchi, Shinji

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PB - Springer Verlag

T2 - 10th International Workshop on Databases in Networked Information Systems, DNIS 2015

Y2 - 23 March 2015 through 25 March 2015

ER -

Miller AA. A photometric machine-learning method to infer stellar metallicity. In Chu W, Kikuchi S, Bhalla S, editors, Databases in Networked Information Systems - 10th International Workshop, DNIS 2015, Proceedings. Springer Verlag. 2015. p. 231-236. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16313-0_17

A photometric machine-learning method to infer stellar metallicity

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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