학술논문

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
Document Type
article
Source
St. Petersburg Polytechnical University Journal: Physics and Mathematics, Vol 16, Iss 4 (2023)
Subject
semi-inclusive deep inelastic scattering
machine learning
neural network
generative-adversarial network
Mathematics
QA1-939
Physics
QC1-999
Language
English
Russian
ISSN
2405-7223
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.