核回归方法在恒星光谱物理参量自动估计中的应用

Kernel Regression Application in Estimating Stellar Fundamental Parameters

恒星大气物理参量(有效温度、表面重力、化学丰度)是导致恒星光谱差异的主要因素。恒星大气物理参量的自动测量是LAMOST等大规模巡天望远镜所产生的海量天体光谱数据自动处理中一个重要研究内容。文章采用两种非线性核回归方法对低分辨率恒星光谱进行3个物理参量的自动估计:核最小二乘回归(KLSR),核PCA回归(KPCR)。实验表明:(1)KLSR与KPCR可以实现光谱到表面有效温度和表面重力的回归,但是KLSR对噪声敏感,KPCR鲁棒性好于前者;(2)对于温度参数估计,两种算法具有相近的估计效果;对于表面重力和化学丰度估计,KPCR优于KLSR和非参数回归方法;(3)KLSR与KPCR方法实现容易,模型的训练速度快,运算复杂度小,适用于恒星光谱物理参量的自动测量。

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 KI.SR and the NPR（Non-parameter regression）; （3） KLSR and KPCR methods are simple and efficient for the stellar spectral parameter estimation.