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Comparison of Approaches to Photometric Redshift Estimation of Quasars

Published online by Cambridge University Press:  17 August 2016

Yang Tu ,
Yan-Xia Zhang ,
Yong-Heng Zhao  and
Hai-Jun Tian
Yang Tu
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China email: tyang@bao.ac.cn
Yan-Xia Zhang
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China email: tyang@bao.ac.cn
Yong-Heng Zhao
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China email: tyang@bao.ac.cn
Hai-Jun Tian
Affiliation:
Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, 100012, Beijing, P.R.China email: tyang@bao.ac.cn
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Abstract

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We probe many kinds of approaches used for photometric redshift estimation of quasars, including KNN (K-nearest neighbor algorithm), Lasso (Least Absolute Shrinkage and Selection Operator), PLS (Partial Least Square regression), ridge regression, SGD (Stochastic Gradient Descent) and Extra-Trees.

Keywords

Photometric redshiftsquasarsdata miningmachine learning
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Symposium S319: Galaxies at High Redshift and Their Evolution Over Cosmic Time , August 2015 , pp. 146
Copyright
Copyright © International Astronomical Union 2016 

References

Yanxia, Zhang, He, Ma, Nanbo, Peng, Yongheng, Zhao, Xue-bing, Wu, AJ 2013 146, 22 (10pp)Google Scholar