Neural network model for proton–proton collision at high energy

Developments in artificial intelligence (AI) techniques and their applications to physics have made it feasible to develop and implement new modeling techniques for high-energy interactions. In particular, AI techniques of artificial neural networks (ANN) have recently been used to design and implement more effective models. The primary purpose of this paper is to model the proton–proton (p–p) collision using the ANN technique. Following a review of the conventional techniques and an introduction to the neural network, the paper presents simulation test results using an p–p based ANN model trained with experimental data. The p–p based ANN model calculates the multiplicity distribution of charged particles and the inelastic cross section of the p–p collision at high energies. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness.