College of Sciences
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 pat- terns 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 Electron-Ion Collider.
Applied Mathematics | Computer Sciences | Physics
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Alanazi, Yasir; Sato, N.; Liu, Tianbo; Melnitchouk, W.; Kuchera, Michelle P.; Pritchard, Evan; Robertson, Michael; Strauss, Ryan; Velasco, Luisa; and Li, Yaohang, "End-to-End Physics Event Generator" (2021). College of Sciences Posters. 7.