JET ANALYSIS BY NEURAL NETWORKS IN HIGH-ENERGY HADRON-HADRON COLLISIONS

We study the possibility to employ neural networks to simulate jet clu stering procedures in high energy hadron-hadron collisions. We concent rate our analysis on the Fermilab Tevatron energy and on the k(perpend icular to) algorithm. We employ both supervised and unsupervised neura l networks. In the first case we consider a multilayer feed-forward ne twork trained by the backpropagation algorithm: our results show that these networks can satisfactorily simulate the relevant features of th e k(perpendicular to) algorithm. We consider also unsupervised learnin g, where the neural network autonomously organizes the events in clust ers. The results of this analysis are discussed and compared with the supervised approach.