STI Publications - View Publication Form #16218
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Publication Information
| Title | Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) | ||||
| 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 E | ||||
| Author(s) | Yasir Alanazi, Nobuo Sato, Tianbo Liu, Wally Melnitchouk, Michelle Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li | ||||
| Publication Date | August 2021 | ||||
| Document Type | Meeting, Proceedings | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Theory & Comp Physics / THEORY CENTER / THEORY CENTER | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | No | ||||
| Supported by Jefferson Lab LDRD Funding | Yes | ||||
| LDRD Number(s) |
2019-LDRD-13
2020-LDRD-18
|
Meeting / Conference
| Meeting Name | 29th International Joint Conference on Artificial Intelligence |
| Meeting Date | 7/11/2020 |
| Document Subtype | Contributed Talk |
Proceedings
| Title | Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence |
| Editor(s) | |
| Publisher | |
| Refereed | No |
| Page Number | 2126-2132 |
| Year | 2021 |
Attachments/Datasets/DOI Link
| Document(s) |
IJCAI 20.pdf
(STI Document)
0293.pdf
(Accepted Manuscript)
|
| DOI Link | |
| Dataset(s) | (none) |
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