基于相似性度量的星系光谱红移估计

光谱的自动分析对大规模的光谱巡天有着非常重要的意义。文章提出了一种基于相似性度量的星系光谱红移测量方法。方法中采用主分量分析构造星系光谱的静止模板,利用谱线特征确定观测光谱的红移候选,然后根据红移候选进行观测光谱与模板光谱间的相似性度量,所采取的相似性度量策略类似于证据积累的思想,定义为几个相似证据的加权和,从而降低了观测光谱与模板光谱之间的误匹配,提高了红移估计的正确率。通过实验将所提出方法与基于谱线匹配的方法和传统的交叉相关方法进行了比较,实验结果表明:本文方法的正确率较之基于谱线匹配的方法和传统相关法有较大提高。

Redshift Estimation of Galaxy Spectra Based on Similarity Measure

Automated spectra analysis is desirable and necessary for efficiency of large sky surveys such as SDSS (Sloan digital sky survey), 2DF (2 degree fields) and LAMOST (large sky area multi-object spectroscopic telescope). In the present paper, we present a method for redshift estimation of galaxy spectra based on similarity measure. Firstly, we extract the spectral lines of the observed spectrum using the feature constrains of spectral lines; secondly, the authors determine the redshift candidates of the observed spectrum by spectral line features; then, the similarity between the observed spectrum and the template spectra shifted by each redshift candidate is measured; finally, the candidate of the highest similarity is chosen as the estimated redshift. PCA (principal component analysis) is used to build the static galaxy template spectra. The authors perform PCA for the four template spectra E, S0, Sa and Sb of the normal galaxy and the seven template spectra Sc, Sb1, Sb2, Sb3, Sb4, Sb5 and Sb6 of the starburst galaxy respectively, where the eleven template spectra are presented by Kinney & Calzetti et al. Two eigen-spectra are produced with the variance contribution rate of 99%. The authors choose the two eigen-spectra as the galaxy templates. The similarity measure proposed, which is similar to the evidence accumulation, is defined as the weighted sum of several similarity evidences. It can reduce the influence caused by some error matching. The authors divide the observed spectrum and the template spectrum respectively into several parts, and measure the correlations of the corresponding parts of them, which is chosen as the similarity evidences in the proposed similarity measure. The principle of setting the weights is that the higher the correlation, the higher the corresponding weight. The proposed approach is compared with the method based on spectral line matching and the traditional cross correlation technique by experiments, the results show that the proposed method has a higher correct rate.