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Proceedings Paper

Signal/background classification in a cosmic ray space experiment by a modular neural system
Author(s): Roberto Bellotti; Marcello Castellano; Carlo Nicola De Marzo; Giuseppe Satalino
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Paper Abstract

In the cosmic ray space experiments, the separation of the signal from background is a hard task. Due to the well-known critical conditions that characterize this class of experiments, some changes of the detector performances can be observed during the data taking. As a consequence, differences between the test and real data are found as systematic errors in the classification phase. In this paper, a modular classification system based on neural networks is proposed for the signal/background discrimination task in cosmic ray space experiments, without a priori knowledge of the discriminating feature distributions. The system is composed by two neural modules. The first one is a self organizing map (SOM) that both clusters the real data space in suitable classes of similarity and builds a prototype for each of them; a skilled inspection of the prototypes defines the signal and background. The second one, a multi layer perceptron (MLP) with a single hidden layer, adapts the classification model based on training/test data to the real experimental conditions. The MLP synaptic weights adaptive formation takes into account the labelled real data set as defined in the first system-phase. The modular neural system has been applied in the context of TRAMP-Si experiment, performed on the NASA Balloon-Borne Magnet Facility, for the positron/proton discrimination.

Paper Details

Date Published: 6 April 1995
PDF: 9 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205112
Show Author Affiliations
Roberto Bellotti, Univ. of Bari (Italy)
Marcello Castellano, National Institute of Nuclear Physics (Italy)
Carlo Nicola De Marzo, Univ. of Bari (Italy)
Giuseppe Satalino, National Council of Research (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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