A new strategy for estimating photometric redshifts of quasars

作     者：Yan-Xia Zhang Jing-Yi Zhang Xin Jin Yong-Heng Zhao Yan-Xia Zhang;Jing-Yi Zhang;Xin Jin;Yong-Heng Zhao

作者机构：Key Laboratory of Optical AstronomyNational Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of SciencesBeijing 100049China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2019年第19卷第12期

页      面：223-234页

核心收录：

学科分类：0709[理学-地质学] 07[理学] 0708[理学-地球物理学] 070401[理学-天体物理] 0704[理学-天文学] 0825[工学-航空宇航科学与技术] 

基　　金：funded by the 973 Program (2014CB845700) the National Natural Science Foundation of China (Grant Nos. 11873066 and U1731109) 

主　　题：astronomical databases:catalogs (galaxies:)quasars:general methods:statistical techniques:miscellaneous 

摘      要：Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then a photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and k NN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples, depending on the redshift distribution. The performances based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improves to some extent compared to the original scheme in terms of the percents in |△z|1+zi 0.1 and |△z|1+zi0.2 and mean absolute error. Only given the running speed, k NN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of k NN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by k NN or by Random Rorest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently, it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in |△z|1+zi0.3, there is still large room for further improvement in the photometric redshift estimation.