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.

本研究使用生成对抗网络技术构建事件发生器，模拟电子 - 质子散射中粒子产生情况，避免了关于基础粒子动力学的理论假设。通过开发 Feature-Augmented 和 Transformed GAN（FAT-GAN），成功选择一组粒子动量转换后的特征，用于增强鉴别器的灵敏度，进而忠实地再现电子散射中的最终状态电子动量分布。发展出的技术可以在现有和未来的加速器设施，如电子离子对撞机中发挥重要作用。