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6 April 1995 Standard Model Higgs boson search with neural networks
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Abstract
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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Klas Hultqvist, Richard Jacobsson, Erik Johansson, and T. Malmgren "Standard Model Higgs boson search with neural networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205105
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