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.
@article{arxiv.hep-ph/9502367,
title = {JET ANALYSIS BY NEURAL NETWORKS IN HIGH ENERGY HADRON-HADRON COLLISIONS},
author = {P. De Felice and G. Nardulli and G. Pasquariello},
journal= {arXiv preprint arXiv:hep-ph/9502367},
year = {2016}
}