Hostname: page-component-77c89778f8-9q27g Total loading time: 0 Render date: 2024-07-18T12:21:10.254Z Has data issue: false hasContentIssue false
  • English
  • Français

Statistical analysis of cross-correlation sample of 3XMM-DR4 with SDSS-DR10 and UKIDSS-DR9

Published online by Cambridge University Press:  01 July 2015

Yan-Xia Zhang ,
Yong-Heng Zhao ,
Xue-Bing Wu  and
Hai-Jun Tian
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
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
Xue-Bing Wu
Affiliation:
Department of Astronomy, Peking University100871, Beijing, P.R.China
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 College of Science, China Three Gorges University, 443002 Yichang, P.R.China email: zyx@bao.ac.cn
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We match the XMM-Newton 3XMMi-DR4 catalog with the Sloan Digital Sky Survey (SDSS) Data Release 10 and the United Kingdom Infrared Deep Sky Survey (UKIDSS) Data Release 9. Based on this X-ray/optical/infrared catalog, we probe the distribution of various types of X-ray emitters in the multidimensional parameter space. It is found that quasars, galaxies and stars have some kind distribution rule, especially for stars. The result shows that only using the X-ray/optical features, stars are difficult to discriminate from galaxies and quasars, the added information from infrared band is very helpful to improve the classification result of any classifier. Comparing the classification accuracy of random forests with that of rotation forests, rotation forests show better performance.

Keywords

catalogs - surveys - X-rays: diffuse background - X-rays: galaxies - X-rays: stars
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 372 - 374
Copyright
Copyright © International Astronomical Union 2015 

References

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. 2009, SIGKDD Explorations, 11 (1), 10CrossRefGoogle Scholar
Rodriguez, J. J., Escuela Politecnica Superior, Burgos Univ., Kuncheva, L. I., & Alonso, C. J. 2006, Pattern Analysis and Machine Intelligence, 28 (10), 1619Google Scholar
Zhang, Y., Zhou, X., Zhao, Y., & Wu, X. 2013, AJ, 145, 42CrossRefGoogle Scholar