Support Vector Machines in Analysis of Top Quark Production
High Energy Physics - Experiment
2009-11-07 v1
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.
Keywords
Cite
@article{arxiv.hep-ex/0205069,
title = {Support Vector Machines in Analysis of Top Quark Production},
author = {A. Vaiciulis},
journal= {arXiv preprint arXiv:hep-ex/0205069},
year = {2009}
}
Comments
8 pages, 8 figures, to be published in the proceedings of the "Advanced Statistical Techniques in Particle Physics" conference in Durham, UK (March, 2002)