提出了一种新的恒星大气物理参数自动估计的新方案, 并称之为SVR(Haar)。 由于观测光谱受到大量宇宙辐射、 大气和观测设备等引起的噪声干扰, 且这种噪声干扰往往是其中的频率较高成分。 所以该方案的基本思想是首先使用Haar小波剔除高频噪声成份, 以提高恒星大气物理参数估计的准确性; 然后使用支持向量机回归方法(SVR)对恒星参数做出估计, 该方法能通过ε不敏感域进一步提高对光谱微小畸变和干扰的容许能力, 增强解决方案的鲁棒性。 为了验证SVR(Haar)方案的有效性, 针对相关研究中的权威模拟恒星光谱和SLOAN发布的实测光谱, 以及文献中的典型处理方法, 做了大量比较实验。 实验结果表明, 所提出的SVR(Haar)恒星参数估计方案比文献中常用的主成分分析和非参数回归模型均要好。

A novel scheme SVR(Haar) is proposed in the present work for automatically estimating the physical parameters of stellar spectra. The observed spectrum is disturbed usually by noise which is caused by the universe radiation, the atmosphere and observation equipment. Furthermore, the noise usually is the component of the spectrum with higher frequency. Therefore, we propose to extract features with Haar wavelet by removing higher frequency components. Researches show that this procedure can improve the accuracy of the estimation. Secondly, the support vector regression model is employed for estimating physical parameters of the stellar spectra. In this method, the ε insensitive domain techniques can further improve the probability to the slight distortion of the spectrum from imperfect calibration, and enhance the robustness of the proposed scheme. To check the effectiveness of the proposed scheme SVR(Haar), we did experiments extensively on authoritative simulated stellar spectra and real spectra observed by SLOAN, and compared it with the typical methods in the literature. The results show that the SVR(Haar) is better than the principal component analysis and non-parametric regression model in the literature.