  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.
