Support Vector Machines in Analysis of Top Quark Production

Details Support Vector Machines in Analysis of Top Quark Production Support Vector Machines in Analysis of Top Quark Production

2002-05-21

arxiv.org/abs/hep-ex/0205069v1

Nucl.Instrum.Meth. A502 (2003) 492-494

Physics

High Energy Physics

High Energy Physics - Experiment

8 pages, 8 figures, to be published in the proceedings of the "Advanced Statistical Techniques in Particle Physics" conference

Scientific paper

10.1016/S0168-9002(03)00479-0

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.

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