Support Vector Machines in Analysis of Top Quark Production

Abstract

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a relatively new way to solve pattern recognition problems and has several advantages over methods such as neural networks. The SVM approach is described and compared to a conventional analysis for the case of identifying top quark signal events in the dilepton decay channel amidst a large number of background events.

8 pages, 8 figures, to be published in the proceedings of the "Advanced Statistical Techniques in Particle Physics" conference in Durham, UK (March, 2002)

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• Support Vector Machines and generalisation in HEP
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