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T. McGlynn, L. Angelini, M. Corcoran, S. Drake (NASA/GSFC/USRA), E. Winter (NASA/GSFC/SSAI), W. Pence, N. White (NASA/GSFC), R. Hanisch, R. White, A. Suchkov, M. Donahue, M. Postman (ST ScI), F. Genova, F. Ochsenbein, P. Fernique, M. Wenger (CDS)
We present initial results in building an automated X-ray classifier. Users of ClassX will submit a list of X-ray sources for classification. ClassX will then use exising information resources on the Web to extract characteristics for these sources, organize this information into coherent source dossiers, and submit these to a hierarchical classifier. We describe our initial results regarding the efficiency of the classifier, the characteristics which seem most useful in classifying sources, comparison of classifier approaches, identification of counterparts to X-ray sources, and sensitivity of the classifier to source brightness and other variables. In this early phase of the ClassX project, information from the HEASARC, USNO A2, GSC2, 2MASS and VizieR is used for the classification. Ultimately we anticipate using many more resources in our classifier with image data as well as catalogs. To date we have focussed on classifying datasets from the ROSAT mission -- the WGACAT and RASS Faint Source catalog, but the goal of ClassX is to support classifications from other missions.
ClassX is intended to explore many of the issues surrounding the development of the Virtual Observatory. correlations of distributed data, access to heterogeneous samples and data transport over the Web. We anticipate that our experience will aid the design of the VO.
ClassX is supported by NASA AISR Grant NAG 5-11019.
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The author(s) of this abstract have provided an email address for comments about the abstract: tam@lheapop.gsfc.nasa.gov