Use of neural network classifiers in high-energy physics: search for the Higgs boson

Abstract

In this work we explore the possibility of using neural network classifiers for the analysis of high-energy physics experimental data. The study deals with the search for the Minimal Standard Model Higgs boson using the data collected in 1990 by the DELPHI detector at LEP. A set of multilayer perceptrons was trained, using Monte Carlo simulated data, to discriminate between Higgs events and the most important background processes. As the signal-to- background ratio is very low it is very important to define suitable selection criteria to reject background events and to select the Higgs events with the best possible efficiency. The use of neural network classifiers allows us to obtain a very good detection efficiency for Higgs events and a complete rejection of all background events.