Using a neural network in the search for the Higgs boson

The search for the Standard Model Higgs boson in high energy e+e− collisions requires analysis techniques which efficiently discriminate against the very large background. A classifier based on a feed-forward neural network has been extensively used in a search in the channel where the Higgs boson is produced in association with neutrinos. The method has significantly improved the sensitivity of the search. With a simple preselection based on event topology followed by a neural network we have obtained a combined background rejection factor of more than 29 000 and a selection efficiency for Higgs particle events of 54%, assuming a mass of 55 GeV/c2 for the Higgs boson. We describe here the details of the analysis with emphasis on the neural network.