| Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS |
| |
| 作者姓名： | Dan Wang Yan-Xia Zhang Chao Liu Yong-Heng Zhao |
| |
| 作者单位： | [1]National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012 [2]Graduate School of Chinese Academy of Sciences, Beijing 100049 |
| |
| 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.
|
| 关 键 词： | 星系 红移星系 远距离星系 数据分析 |
| 收稿时间： | 2007-03-28 |
| 修稿时间： | 2007-05-12 |
Two Novel Approaches for Photometric Redshift Estimation based on SDSS and 2MASS |
| |
| Abstract: | |
| |
| Keywords: | galaxies distances and redshifts - galaxies general - methods data analysis - techniques photometric |
| 本文献已被 维普 等数据库收录！ |
|
