A machine-learning method to infer fundamental stellar parameters from photometric light curves

A. A. Miller, J. S. Bloom, J. W. Richards, Y. S. Lee, D. L. Starr, Nathaniel Butler, S. Tokarz, N. Smith, J. A. Eisner

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >109 photometrically cataloged sources, yet modern spectroscopic surveys are limited to ∼few× 106 targets. As we approach the Large Synoptic Survey Telescope era, new algorithmic solutions are required to cope with the data deluge. Here we report the development of a machine-learning framework capable of inferring fundamental stellar parameters (T eff, log g, and [Fe/H]) using photometric-brightness variations and color alone. A training set is constructed from a systematic spectroscopic survey of variables with Hectospec/Multi-Mirror Telescope. In sum, the training set includes ∼9000 spectra, for which stellar parameters are measured using the SEGUE Stellar Parameters Pipeline (SSPP). We employed the random forest algorithm to perform a non-parametric regression that predicts T eff, log g, and [Fe/H] from photometric time-domain observations. Our final optimized model produces a cross-validated rms error (RMSE) of 165 K, 0.39 dex, and 0.33 dex for T eff, log g, and [Fe/H], respectively. Examining the subset of sources for which the SSPP measurements are most reliable, the RMSE reduces to 125 K, 0.37 dex, and 0.27 dex, respectively, comparable to what is achievable via low-resolution spectroscopy. For variable stars this represents a ≈12%-20% improvement in RMSE relative to models trained with single-epoch photometric colors. As an application of our method, we estimate stellar parameters for ∼54,000 known variables. We argue that this method may convert photometric time-domain surveys into pseudo-spectrographic engines, enabling the construction of extremely detailed maps of the Milky Way, its structure, and history.

Original languageEnglish (US)
Article number122
JournalAstrophysical Journal
Volume798
Issue number2
DOIs
StatePublished - Jan 10 2015

Fingerprint

machine learning
light curve
education
telescopes
color
variable stars
set theory
engines
regression analysis
engine
brightness
spectroscopy
time measurement
method
parameter
histories
mirrors
estimates
history

Keywords

  • methods: data analysis
  • methods: statistical
  • stars: general
  • stars: statistics
  • stars: variables: general
  • surveys

ASJC Scopus subject areas

  • Space and Planetary Science
  • Astronomy and Astrophysics

Cite this

Miller, A. A., Bloom, J. S., Richards, J. W., Lee, Y. S., Starr, D. L., Butler, N., ... Eisner, J. A. (2015). A machine-learning method to infer fundamental stellar parameters from photometric light curves. Astrophysical Journal, 798(2), [122]. https://doi.org/10.1088/0004-637X/798/2/122

A machine-learning method to infer fundamental stellar parameters from photometric light curves. / Miller, A. A.; Bloom, J. S.; Richards, J. W.; Lee, Y. S.; Starr, D. L.; Butler, Nathaniel; Tokarz, S.; Smith, N.; Eisner, J. A.

In: Astrophysical Journal, Vol. 798, No. 2, 122, 10.01.2015.

Research output: Contribution to journalArticle

Miller, AA, Bloom, JS, Richards, JW, Lee, YS, Starr, DL, Butler, N, Tokarz, S, Smith, N & Eisner, JA 2015, 'A machine-learning method to infer fundamental stellar parameters from photometric light curves', Astrophysical Journal, vol. 798, no. 2, 122. https://doi.org/10.1088/0004-637X/798/2/122
Miller, A. A. ; Bloom, J. S. ; Richards, J. W. ; Lee, Y. S. ; Starr, D. L. ; Butler, Nathaniel ; Tokarz, S. ; Smith, N. ; Eisner, J. A. / A machine-learning method to infer fundamental stellar parameters from photometric light curves. In: Astrophysical Journal. 2015 ; Vol. 798, No. 2.
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AU - Butler, Nathaniel

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