A UNIVERSAL NEURAL NETWORK REPRESENTATION FOR HADRON–HADRON INTERACTIONS AT HIGH ENERGY

Abstract

An efficient neural network (NN) has been designed to simulate the hadron–hadron interaction at high energy. Two cases have been considered simultaneously, the proton–proton (p–p) and the pion–proton (π-p) interactions. The neural network has been trained to produce the charged multiplicity distribution for both cases based on samples from the overlapping functions. The trained NN shows a good performance in matching the trained distributions. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The robustness of the designed NN in the presence of uncertainties, in the overlapping functions has been demonstrated.