Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

Yasir Alanazi, Nobuo Sato, Tianbo Liu, Wally Melnitchouk, Pawel Ambrozewicz, Florian Hauenstein, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan R. Strauss, Luisa Velasco, Yaohang Li

IJCAI 2021 pp. 2126-2132

doi:10.24963/IJCAI.2021/293 /ijcai/2021/alanazi2021ijcai-simulation/

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.

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Text

Alanazi et al. "Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/293

Markdown

[Alanazi et al. "Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/alanazi2021ijcai-simulation/) doi:10.24963/IJCAI.2021/293

BibTeX

@inproceedings{alanazi2021ijcai-simulation,
  title     = {{Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)}},
  author    = {Alanazi, Yasir and Sato, Nobuo and Liu, Tianbo and Melnitchouk, Wally and Ambrozewicz, Pawel and Hauenstein, Florian and Kuchera, Michelle P. and Pritchard, Evan and Robertson, Michael and Strauss, Ryan R. and Velasco, Luisa and Li, Yaohang},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {2126-2132},
  doi       = {10.24963/IJCAI.2021/293},
  url       = {https://mlanthology.org/ijcai/2021/alanazi2021ijcai-simulation/}
}