Estimating Photometric Redshifts with Artificial Neural Networks and Multi-Parameters

作     者：Li-Li Li Yan-Xia Zhang Yong-Heng Zhao Da-Wei Yang 

作者机构：National Astronomical Observatories Chinese Academy of Sciences Beijing 100012 College of Physics Science and Information Engineering Hebei Normal University Shijiazhuang 050016 

出 版 物：《Chinese Journal of Astronomy and Astrophysics》 (中国天文和天体物理学报（英文版）)

年 卷 期：2007年第7卷第3期

页      面：448-456页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：the National Natural Science Foundation of China 

主　　题：galaxies fundamental parameters - techniques photometric - method dataanalysis 

摘      要：We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 （SDSS DR2） Galaxy Sample using artificial neural networks （ANNs）. Different input sets based on various parameters （e.g. magnitude, color index, flux information） are explored. Mainly, parameters from broadband photometry are utilized and their performances in redshift prediction are compared. While any parameter may be easily incorporated in the input, our results indicate that using the dereddened magnitudes often produces more accurate photometric redshifts than using the Petrosian magnitudes or model magnitudes as input, but the model magnitudes are superior to the Petrosian magnitudes. Also, better performance resuits when more effective parameters are used in the training set. The method is tested on a sample of 79 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddened magnitudes, the rms error in redshift estimation is σz = 0.020184. The ANN is highly competitive tool compared to the traditional template-fitting methods when a large and representative training set is available.