恒星大气物理参数估计中的稀疏特征提取

随着斯隆数字巡天项目(SDSS)、欧空局GAIA和我国大天区面积多目标光纤光谱天文望远镜(LAMOST)等项目的相继实施，拥有的恒星光谱数据量急速增加，由此导致基于光谱的恒星大气物理参数自动测量方法的研究成为天文光谱分析的重要课题之一1]。探讨了恒星光谱特征提取的方法(Haar+lasso)，其基本思想是首先使用Haar小波包对原始光谱进行多尺度分解，去除高频系数，选取低频系数作为光谱信息的描述;再采用lasso算法提取最优的特征; 最后将最优特征输入非参数回归模型中对恒星大气参数进行自动测量。Haar小波可以较好地去除原始光谱信号中的高频噪声，对全频谱数据进行降维。lasso算法可以进一步剔除数据冗余, 提取与物理参数相关度较强的特征。Haar+lasso方法提高了物理参数自动测量的准确性和运行效率。我们采用本文方案对SLOAN发布的40 000个恒星子样本的物理参数进行测量，三个物理参数的平均绝对误差为： log Teff: 0.007 1 dex，log g: 0.225 2 dex和Fe/H]: 0.199 6 dex。同现有相关文献的实验结果相比，该方案可以获得更准确的物理参数。

A New Method of Sparse Feature Extraction for Stellar Spectra

The authors propose a novel method of feature extraction for stellar spectra parameterization. The basic procedures are: First, stellar spectra are decomposed by multi-scale Harr wavelet and the coefficients with high-frequency are rejected. Secondly, the optimal features are detected by the lasso algorithm. Finally, we input the optimal feature vector to non-parametric regression model to estimate the atmospheric parameters. Haar wavelet can remove the high-frequency noise from the stellar spectrum. Lasso algorithm can further compress data by analyzing their significance on parameterization and removing redundancy. Experiments show that the proposed Haar+lasso method improves the accuracy and efficiency of the estimation. The authors used this scheme to estimate the atmospheric parameters from a subsample of some 40 000 stellar spectra from SDSS. The accuracies of our predictions (mean absolute errors) for each parameter are 0.007 1 dex for log Teff, 0.225 2 dex for log g, and 0.199 6 dex for Fe/H]. Compared with the results of the existing literature, this scheme can derive more accurate atmospheric parameters.