标题：Stellar atmospheric parameter estimation using Gaussian process regression
作者：Bu, Yude;Pan, Jingchang
作者机构：[Bu, Y] School of Mathematics, and Statistics, Shandong University, Weihai Shandong, 264209, China;[ Pan, J] School of Mechanical, Electrical, and Inf 更多
通讯作者地址：[Bu, YD]Shandong Univ, Sch Math & Stat, Weihai 264209, Shandong, Peoples R China.
来源：Monthly notices of the Royal Astronomical Society
关键词：methods: data analysis;methods: numerical;stars: abundances;stars: fundamental parameters
摘要：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.