Abstract
Observations of present and future Xโray telescopes include a large number of ipitous sources of unknown types. They are a rich source of knowledge about Xโray dominated astronomical objects, their distribution, and their evolution. The large number of these sources does not permit their individual spectroscopical followโup and classification. Here we use Chandra MultiโWavelength public data to investigate a number of statistical algorithms for classification of Xโray sources with optical imaging followโup. We show that up to statistical uncertainties, each class of Xโray sources has specific photometric characteristics that can be used for its classification. We assess the relative and absolute performance of classification methods and measured features by comparing the behaviour of physical quantities for statistically classified objects with what is obtained from spectroscopy. We find that among methods we have studied, multiโdimensional probability distribution is the best for both classifying source type and redshift, but it needs a sufficiently large input (learning) data set. In absence of such data, a mixture of various methods can give a better final result.We discuss some of potential applications of the statistical classification and the enhancement of information obtained in this way. We also assess the effect of classification methods and input data set on the astronomical conclusions such as distribution and properties of Xโray selected sources. (ยฉ 2008 WILEYโVCH Verlag GmbH & Co. KGaA, Weinheim)