The mass window open for the standard model Higgs boson at LEP1 is at present restricted to a region where the production rate is very small. Moreover, Higgs particle events in this region are very difficult to separate from the background, which is why new analysis techniques are needed. We have employed a classifier based on a feed-forward neural network for the discrimination against the very large background. With a simple preselection followed by a neural network we have obtained a combined background rejection factor of about 29,000 and a detection efficiency of about 54% for a Higgs particle with a mass of 55 GeV/c². With a different transformation of the input variables to the network, the detection efficiency was improved by a factor of 1.10, with the same background rejection.

Date Published: 6 April 1995
PDF: 6 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205105

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)