Abstract
A separation of a signal of various physics processes from an overwhelming background is one of the most important problems in contemporary high-energy physics. The application of various multivariate statistical methods, such as the neural-network method, has become one of the popular steps toward optimizing relevant analyses. The choice of optimum variables that would disclose distinctions between a signal and a background is one of the important elements in the application of neural networks. A universal method for determining an optimum set of such kinematical variables is described in the present article. The method is based on an analysis of Feynman diagrams contributing to signal and background processes. This method was successfully implemented in searches for single top-quark production with the D0 detector (Tevatron, Fermilab) in analyzing Run I and Run II data. Brief recommendations concerning an optimum implementation of the neural-network method in physics analysis are given on the basis of experience gained in searches for single top-quark production with the D0 detector.
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