Spectral Synthesis via Mean Field approach to Independent Component Analysis  

作　　者：Ning Hu Shan-Shan Su Xu Kong 

机构地区：[1]CAS Key Laboratory for Research in Galaxies and Cosmology, Department of Astronomy, University of Science and Technology of China

出　　处：《Research in Astronomy and Astrophysics》2016年第3期51-62,共12页天文和天体物理学研究（英文版）

基　　金：supported by the Strategic Priority Research Program "The Emergence of Cosmological Structures" of the Chinese Academy of Sciences (No. XDB09000000);the National Basic Research Program of China (973 Program) (2015CB857004);the National Natural Science Foundation of China (NSFC, Nos. 11225315, 1320101002, 11433005 and 11421303)

摘　　要：We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis（MF-ICA） in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components（ICs）, and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used：（1） parameter recovery for simulated galaxies,（2） comparison with parameters estimated by other methods, and（3） consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis（MF-ICA） in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components（ICs）, and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used：（1） parameter recovery for simulated galaxies,（2） comparison with parameters estimated by other methods, and（3） consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.

关 键 词：methods： data analysis -- methods： statistical -- galaxies： evolution -- galaxies： fundamentalparameters -- galaxies： stellar content 

分 类 号：P152[天文地球—天文学]