Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Document Type
Conference Paper
Publication Date
2021
DOI
10.24963/ijcai.2021/293
Publication Title
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Pages
2126-2132
Conference Name
Thirtieth International Joint Conference on Artificial Intelligence, 19-27 August 2021, Virtual, Montreal
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.
Rights
© 2021 International Joint Conferences on Artificial Intelligence Organization. All rights reserved.
Original Publication Citation
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. (2021). Simulation of electron-proton scattering events by a feature-augmented and transformed generative adversarial network (FAT-GAN). In Z.-H. Zhou (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2126-2132). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2021/293
Repository Citation
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. (2021). Simulation of electron-proton scattering events by a feature-augmented and transformed generative adversarial network (FAT-GAN). In Z.-H. Zhou (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (pp. 2126-2132). International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2021/293
ORCID
0000-0002-1265-2212 (Hauenstein)