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19 July 2010 Approaches for photometric redshift estimation of quasars from SDSS and UKIDSS
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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.
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Dan Wang, Yan-xia Zhang, and Yong-heng Zhao "Approaches for photometric redshift estimation of quasars from SDSS and UKIDSS", Proc. SPIE 7740, Software and Cyberinfrastructure for Astronomy, 77402O (19 July 2010); doi: 10.1117/12.856813; https://doi.org/10.1117/12.856813
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