Research Papers
Support Vector Machines for Photometric Redshift Estimation from Broadband Photometry
Authors:
Dan Wang ,
National Astronomical Observatories, Chinese Academy of Sciences, China
Yanxia Zhang,
National Astronomical Observatories, Chinese Academy of Sciences, China
Yongheng Zhao
National Astronomical Observatories, Chinese Academy of Sciences, China
Abstract
Photometric redshifts have been regarded as efficient and effective measures for studying the statistical properties of galaxies and their evolution. In this paper, we introduce SVM_Light, a freely available software package using support vector machines (SVM) for photometric redshift estimation. This technique shows its superiorities in accuracy and efficiency. It can be applied to huge volumes of datasets, and its efficiency is acceptable. When a large representative training set is available, the results of this method are superior to the best ones obtained from template fitting. The method is used on a sample of 73,899 galaxies from the Sloan Digital Sky Survey Data Release 5. When applied to processed data sets, the RMS error in estimating redshifts is less than 0.03. The performances of various kernel functions and different parameter sets have been compared. Parameter selection and uniform data have also been discussed. Finally the strengths and weaknesses of the approach are summarized.
How to Cite:
Wang, D., Zhang, Y. & Zhao, Y., (2007). Support Vector Machines for Photometric Redshift Estimation from Broadband Photometry. Data Science Journal. 6, pp.S474–S480. DOI: http://doi.org/10.2481/dsj.6.S474
Published on 22 Aug 2007.
Peer Reviewed
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