Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)

@article{Alanazi2021SimulationOE,
  title={Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)},
  author={Yasir Alanazi and Nobuo Sato and Tianbo Liu and W. Melnitchouk and Michelle P. Kuchera and Evan Pritchard and Michael Robertson and Ryan R. Strauss and Luisa Velasco and Yaohang Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2001.11103}
}
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… Expand

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