Automated classification of rosat sources using heterogeneous multiwavelength source catalogs

We describe an online system for automated classification of X-ray sources, ClassX, and we present preliminary results of classification of the three major catalogs of ROSAT sources, ROSAT All-Sky Survey (RASS) Bright Source Catalog, RASS Faint Source Catalog, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, active galactic nuclei, and clusters of galaxies. ClassX is based on a machine-learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is "trained'' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in online data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance, and we present the respective validation procedures. On the basis of both internal validation and external verification, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.