Feature-Augmented and Transformed GAN (FAT-GAN) is implemented as 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. FAT-GAN is designed 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 detailed description of FAT-GAN can be found at https://arxiv.org/abs/2001.11103 (Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li).
The FAT-GAN script reads event generated by Pythia and trains the FAT-GAN accordingly.
To run the script: * python FAT-GAN.py
Contact: yaohang@cs.odu.edu (Yaohang Li)