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Particle discrimination using matched filters and expert neural networks

A particle discrimination problem in high-energy physics is addressed by optimal linear filtering and neural processing on experimental data acquired from a highly segmented calorimeter, which is a detector that measures the energy of the incoming particles. It is shown that both approaches are able to identify impurities that typically appear in the data sample and achieve discrimination efficiencies higher than 98%.