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Proceedings Paper

Neural networks with statistical preprocessing for particle discrimination in high-energy physics
Author(s): Enrico Pasqualucci
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Paper 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.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205114
Show Author Affiliations
Enrico Pasqualucci, INFN-Istituto Nazionale di Fisica Nucleare (Italy)

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

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