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Proceedings Paper

Approaches for photometric redshift estimation of quasars from SDSS and UKIDSS
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Paper Abstract

We investigate two methods: kernel regression and nearest neighbor algorithm for photometric redshift estimation with the quasar samples from SDSS (the Sloan Digital Sky Survey) and UKIDSS (the UKIRT Infrared Deep Sky Survey) databases. Both kernel regression and nearest neighbor algorithm belong to the family of instance-based learning algorithms, which store all the training examples and "delay learning" until prediction time. The major difference between the two algorithms is that kernel regression is a weighted average of spectral redshifts of the neighbors for a query point while nearest neighbor algorithm utilizes the spectral redshift of the nearest neighbor for a query point. Each algorithm has its own advantage and disadvantage. Our experimental results show that kernel regression obtains more accurate predicting results, and nearest neighbor algorithm shows its superiority especially for more thinly spread data, e.g. high redshift quasars.

Paper Details

Date Published: 19 July 2010
PDF: 10 pages
Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402O (19 July 2010); doi: 10.1117/12.856813
Show Author Affiliations
Dan Wang, National Astronomical Observatories (China)
Yan-xia Zhang, National Astronomical Observatories (China)
Yong-heng Zhao, National Astronomical Observatories (China)


Published in SPIE Proceedings Vol. 7740:
Software and Cyberinfrastructure for Astronomy
Nicole M. Radziwill; Alan Bridger, Editor(s)

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