Abstract
Two aspects of neural-net analysis are addressed: the application of neural nets to physics analysis and the analysis of neural nets. Feed-forward nets with error back-propagation are applied to the search for the standard Higgs boson at LEP 200. New methods to select the most efficient variables in such a classification task and to analyse the nets are presented. The sensitivity of the nets for systematic effects is studied extensively. The efficiencies of the neural nets are found to be significantly better than those of standard methods.
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