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Title: A MACHINE-LEARNING METHOD TO INFER FUNDAMENTAL STELLAR PARAMETERS FROM PHOTOMETRIC LIGHT CURVES

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are >10{sup 9} photometrically cataloged sources, yet modern spectroscopic surveys are limited to ∼few× 10{sup 6} 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 {sub 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 {sub 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 {sub 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 thismore » 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.« less
Authors:
 [1] ; ; ;  [2] ;  [3] ;  [4] ;  [5] ; ;  [6]
  1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 169-506, Pasadena, CA 91109 (United States)
  2. Department of Astronomy, University of California, Berkeley, CA 94720-3411 (United States)
  3. Department of Astronomy and Space Science, Chungnam National University, Daejeon 305-764 (Korea, Republic of)
  4. School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281 (United States)
  5. Smithsonian Astrophysical Observatory, Cambridge, MA 02138 (United States)
  6. Steward Observatory, University of Arizona, Tucson, AZ 85721 (United States)
Publication Date:
OSTI Identifier:
22364604
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 798; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; BRIGHTNESS; COLOR; COMPARATIVE EVALUATIONS; DATA ANALYSIS; DIAGRAMS; EMISSION SPECTRA; IRON; MATHEMATICAL SOLUTIONS; MILKY WAY; PHOTOMETRY; RANDOMNESS; STATISTICS; TELESCOPES; VARIABLE STARS; VISIBLE RADIATION