[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.
Liu
Rong
R
School of Science, Xidian University, Xi'an 710071, China.
Qiao
Xue-Jun
XJ
Duan
Fu-Qing
FQ