Paper
24 September 2012 Support vector machines for photometric redshift measurement of quasars
Hongwen Zheng, Yanxia Zhang
Author Affiliations +
Abstract
Based on photometric and spectroscopic data of quasars from SDSS DR7 and UKDISS DR7, support vector machines (SVM) is applied to predict photometric redshifts of quasars. Different input patterns are tried and the best pattern is presented. Comparing the results using optical data with that using optical and infrared data, the experimental results show that the accuracy improves with data from more bands. In addition, the quasar sample is firstly clustered into two groups by one-class SVM, then the photometric redshifts of the two groups are separately estimated by means of SVM. The results based on the whole sample and the combined results from the two groups are comparable.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hongwen Zheng and Yanxia Zhang "Support vector machines for photometric redshift measurement of quasars", Proc. SPIE 8451, Software and Cyberinfrastructure for Astronomy II, 845133 (24 September 2012); https://doi.org/10.1117/12.925761
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Galactic astronomy

Astronomy

Data modeling

Infrared radiation

Stars

Statistical analysis

Neural networks

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