基于二元PCA特征谱的星系光谱成分快速扣除

实际观测天体目标光谱如超新星和活动星系核光谱常常混有寄主星系成分，这对目标天体光谱的类别和性质证认识别会造成困难。文章提出了一种快速有效的称为二元PCA特征谱分解的星系扣除算法。该算法首先计算了星系样本模板库和超新星样本模板库各自的PCA特征光谱，然后对特征光谱组通过正交变换得到混合空间的一组标准正交基，进而利用混合光谱在该标准正交基上的分解系数计算该光谱在原特征光谱组的分解系数，获得星系超新星混合光谱的快速分解,系数计算也可通过SVD矩阵分解得到，但计算效率较低。实验表明，该方法优于常用的直接PCA投影重构分解方法，与另一种χ2模板拟合方法扣除星系成分相比，在保持分解效果基本不变的前提下，时间消耗则大大降低，从而使该方法可应用到大规模光谱数据处理中。

Galaxy Spectrum Subtraction of a Mixed Spectrum Based on Two Class PCA Eigen-Spectra

The authors present a new method called two class PCA for decomposing the mixed spectra, namely, for subtracting the host galaxy contamination from each SN spectrum. The authors improved the quality of reconstructed galaxy spectrum and computational efficiency, and these improvements were realized because we used both the PCA eigen spectra of galaxy templates library and SN templates library to model the mixed spectrum. The method includes mainly three steps described as follows. The first step is calculating two class PCA eigen spectra of galaxy templates and SN templates respectively. The second step is determining all reconstructed coefficients by the SVD matrix decomposition or orthogonal transformation. And the third step is computing a reconstructed galaxy spectrum and subtracting it from each mixed spectrum. Experiments show that this method can obtain an accurate decomposition of a mixed synthetic spectrum, and is a method with low time-consumption to get the reliable SN spectrum without galaxy contamination and can be used for spectral analysis of large amount of spectra. The time consumption using our method is much lower than that using χ2-template fitting for a spectrum.