Support Vector Machines for Photometric Redshift Estimation from Broadband Photometry

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.