Journal Article

Stellar atmospheric parameter estimation using Gaussian process regression

Yude Bu and Jingchang Pan

in Monthly Notices of the Royal Astronomical Society

Volume 447, issue 1, pages 256-265
ISSN: 0035-8711
Published online December 2014 | e-ISSN: 1365-2966 | DOI: http://dx.doi.org/10.1093/mnras/stu2063
Stellar atmospheric parameter estimation using Gaussian process regression

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As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.

Keywords: methods: data analysis; methods: numerical; stars: abundances; stars: fundamental parameters

Journal Article.  6889 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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