Simulation of semi-inclusive deep inelastic lepton scattering on a proton at energies of 20 – 100 GeV on the basis of the Generative-Adversarial Neural Network

Abstract

This paper continues a series of articles devoted to developing the capabilities of a deep inelastic lepton-proton scattering event generator based on the generative adversarial network (GAN). The investigation has focused on semi-inclusive reactions of deep inelastic scattering and, particularly, on hadron registration. The results confirmed that GAN could accurately generate distributions of physical properties of leptons and hadrons. It worked for different types of leptons and hadrons in the range of initial energies from 20 to 100 GeV in the center-of-mass system. The GAN demonstrated to preserve the inherent correlation between the characteristics of leptons and protons.

Keywords

• semi-inclusive deep inelastic scattering
• machine learning
• neural network
• generative-adversarial network