Photometric redshift estimation for quasars by integration of KNN and SVM

	Photometric redshift estimation for quasars by integration of KNN and SVM
	Han, Bo1 ; Ding, Hong-Peng 1; Zhang, Yan-Xia2 ; Zhao, Yong-Heng2
刊名	RESEARCH IN ASTRONOMY AND ASTROPHYSICS
	2016-05-01
卷号	16 期号:5
关键词	catalogs galaxies: distances and redshifts methods: statistical quasars: general surveys techniques: photometric
英文摘要	The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding z(phot). Our analysis has found two dense regions with catastrophic failure, one in the range of z(phot) is an element of [0.3, 1.2] and the other in the range of z(phot) is an element of [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different vertical bar Delta z vertical bar ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (7 1. 9 6 %, 83.78%, 89.73% and 0.204).
收录类别	SCI
语种	英语
WOS记录号	WOS:000375793000005
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/5371]
专题	国家天文台_光学天文研究部
作者单位	1.Wuhan Univ, Int Sch Software, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China
推荐引用方式 GB/T 7714	Han, Bo,Ding, Hong-Peng,Zhang, Yan-Xia,et al. Photometric redshift estimation for quasars by integration of KNN and SVM[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2016,16(5).
APA	Han, Bo,Ding, Hong-Peng,Zhang, Yan-Xia,&Zhao, Yong-Heng.(2016).Photometric redshift estimation for quasars by integration of KNN and SVM.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,16(5).
MLA	Han, Bo,et al."Photometric redshift estimation for quasars by integration of KNN and SVM".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 16.5(2016).