A HYBRID NEURAL NETWORK ARCHITECTURE FOR THE CLASSIFICATION OF THE HADRONIC DECAYS OF THEZ0

G. Cosmo and A. de Angelis
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G. Cosmo: Dipartimento di Fisica dell'Universitá di Udine, Via delle Scienze 208, I-33100 Udine, Italy;
A. de Angelis: Dipartimento di Fisica dell'Universitá di Udine, Via delle Scienze 208, I-33100 Udine, Italy;

International Journal of Modern Physics C (IJMPC), 1993, vol. 04, issue 05, 977-981

Abstract: Feed-Forward Neural Networks have shown to be a useful tool for the automatic classification of events in High Energy Physics. A shortcoming of the method is anyway given by the large value of simulated events to be used for training the classifier. In this paper, we describe an alternative Neural Network architecture that allows a substantial reduction of the CPU time spent in the training phase. This architecture has been tested on a complex problem, such as the classification of the hadronic decays of theZ0, and its performance has been compared with that of a Feed-Forward Neural Network.

Date: 1993
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DOI: 10.1142/S0129183193000756

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