Particle selection in a cosmic ray space experiment by neural networks

The study of the human decision-making process has led the investigators to formulate formal paradigms in order to gain knowledge by examples. Among of these, neural networks are high performance paradigms to build up example-based classifiers. In cosmic ray space experiments, a large volume of information for each cosmic ray trigger are produced. Some of the data was visual in nature, other portions contained energy deposition and timing information. The data sets are amenable to conventional analysis techniques but there is no assurance that conventional techniques make full use of subtle correlations and relations amongst the detector responses. In this paper a data analysis, based on neural network classifiers, is presented in order to study cosmic ray propagation and production in the Galaxy, by means of the electron and positron signal, detected in a balloon borne experiment.