Modeling of heavy ion collisions using radial basis function and generalized regression neural networks

Abstract

Artificial neural networks (ANNs) have been applied to heavy ion collisions. In the present work, the possibility of using ANN methods for modeling the multiplicity distributions, P(ns), of shower particles produced from p, d, 4He, 6Li, 7Li, 12C, 16O, and 24Mg interactions with light (CNO) as well as heavy (AgBr) emulsions at 4.5 A GeV/c was investigated. Two different ANN approaches, namely radial basis function neural network (RBFNN) and generalized regression neural network (GRNN), were employed to obtain a mathematical formula describing these collisions. The results from RBFNN and GRNN models showed good agreement with the experimental data. GRNN models have a better performance than the RBFNN models. This study showed that the RBFNN and GRNN models are capable of accurately predicting the P(ns) of shower particles in the training and testing phases.