Publications
Publication Information
| Title | Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) |
| Authors | Yasir Alanazi, Nobuo Sato, Tianbo Liu, Wolodymyr Melnitchouk, Michelle Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li |
| JLAB number | JLAB-THY-20-3136 |
| LANL number | arXiv:2001.11103 |
| Other number | DOE/OR/23177-4903 |
| Document Type(s) | (Meeting) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | Yes |
| LDRD Numbers: | 2019-LDRD-13 2020-LDRD-18 |
| Funding Source: | Nuclear Physics (NP) |
|
Meeting Contributed Talk compiled for 29th International Joint Conference on Artificial Intelligence Proceedings Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence Edited By (2021) Page(s) 2126-2132 | |
| Publication 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 the Jefferson Lab 12 GeV program and the future Electron-Ion Collider. |
| Experiment Numbers: | other |
| Group: | THEORY CENTER |
| Document: | |
| DOI: | https://doi.org/10.24963/ijcai.2021/293 |
| Accepted Manuscript: | 0293.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |