Kernel Regression Application in Estimating Stellar Fundamental Parameters

The three fundamental parameters of stellar atmosphere, i. e. the effective temperature, the surface gravity, and the metallic, determine the continuum and spectral lines in the stellar spectrum. With the development of the modern telescopes such as SDSS, LAMOST projects, the great voluminous spectra demand to explore automatic celestial spectral analysis methods. It is most significant for Galaxy research to develop automatic methods determining the fundamental parameters from stellar spectra data. Two non-linear regression algorithms, kernel least squared regression (KLSR) and kernel PCA regression (KPCR), are proposed for estimating the three parameters in the present paper. The linear regression models, LSR and PCR, are extended to non-linear regression by using a kernel function for the stellar parameter estimation from spectra. Extensive experiments on low resolution spectra data show: (1) KLSR and KPCR methods realize the regression from spectrum to the effective temperature and gravity. KLSR is sensitive to the noise while KPCR is robust than the former. (2) For the effective temperature estimation, the two algorithms perform similarly; and for the gravity and metallic estimation, the KPCR is superior to the KLSR and the NPR(Non-parameter regression); (3) KLSR and KPCR methods are simple and efficient for the stellar spectral parameter estimation.