twonovelapproachesforphotometricredshiftestimationbasedonsdssand2mass

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	twonovelapproachesforphotometricredshiftestimationbasedonsdssand2mass
	Dan Wang; YanXia Zhang; Chao Liu; YongHeng Zhao
刊名	chineseastronomyandastrophysics
	2008
卷号	8 期号:1 页码:119
ISSN号	0275-1062
英文摘要	We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the databases of Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey. We probe the performances of SVMs and KR for different input patterns. Our experiments show that with more parameters considered, the accuracy does not always increase, and only when appropriate parameters are chosen, the accuracy can improve. For different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.
语种	英语
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/53436]
专题	中国科学院国家天文台
作者单位	中国科学院国家天文台
推荐引用方式 GB/T 7714	Dan Wang,YanXia Zhang,Chao Liu,et al. twonovelapproachesforphotometricredshiftestimationbasedonsdssand2mass[J]. chineseastronomyandastrophysics,2008,8(1):119.
APA	Dan Wang,YanXia Zhang,Chao Liu,&YongHeng Zhao.(2008).twonovelapproachesforphotometricredshiftestimationbasedonsdssand2mass.chineseastronomyandastrophysics,8(1),119.
MLA	Dan Wang,et al."twonovelapproachesforphotometricredshiftestimationbasedonsdssand2mass".chineseastronomyandastrophysics 8.1(2008):119.