JET ANALYSIS BY NEURAL NETWORKS IN HIGH ENERGY HADRON-HADRON COLLISIONS
P. De Felice, G. Nardulli, G. Pasquariello
Abstract
We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the k algorithm. We consider both supervised multilayer feed-forward network trained by the backpropagation algorithm and unsupervised learning, where the neural network autonomously organizes the events in clusters.
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