Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Journal
IJCAI International Joint Conference on Artificial Intelligence
Pages
2126-2132
Date Issued
2021
Author(s)
Alanazi Y
Sato N
Liu T
Melnitchouk W
Ambrozewicz P
Hauenstein F
Kuchera M.P
Pritchard E
Robertson M
Strauss R
Velasco L
Li Y.
Abstract
We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of existing and future accelerator facilities, such as the Electron-Ion Collider. © 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.
Other Subjects
Electron scattering; Electrons; Accelerator facilities; Electron momentum; Electron-proton scattering; Event generators; Final state; Network technologies; Particle dynamics; Particle momentum; Particle production; Scattering events; Generative adversarial networks
Type
conference paper