6 April 1995 Neural networks with statistical preprocessing for particle discrimination in high-energy physics
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Abstract
A typical task of an analysis program in high energy physics is the discrimination between different kinds of particles interacting with a detector. Neural networks can be easily used to perform this task. In this paper, the performances of a feed-forward neural network as a particle identifier are studied and compared with results from discriminant analysis. A typical task, the (pi) -(mu) separation at 250 MeV/c is presented as an example application. Experimental data collected during a test run of the KLOE electromagnetic calorimeter are used. The effects of the introduction of a statistical pre-processing on physical variables is studied. It allows us to obtain better results both in terms of learning time and in terms of efficiency and background rejection.
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Enrico Pasqualucci, "Neural networks with statistical preprocessing for particle discrimination in high-energy physics", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205114; https://doi.org/10.1117/12.205114
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