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.

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

ORCID

0000-0002-1265-2212 (Hauenstein)

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