cFAT-GAN: Conditional Simulation of Electron–Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network

Abstract

The recently proposed generative adversarial network (GAN)-based event generator, the Feature Augmented and Transformed GAN (FAT-GAN), has shown an impressive capability of reproducing inclusive electron–proton scattering events at given collision energy. In contrast, many practical applications require the event generator to have the flexibility of allowing users to specify the reaction energy as an input to produce the corresponding synthetic events. In this work, we extend the FAT-GAN framework by conditioning the component neural networks according to the given reaction energy. We demonstrate that this model, referred to as cFAT-GAN, can reliably produce inclusive event feature distributions and correlations for a continuous range of reaction energies by automatically interpolating and extrapolating from a set of trained energies. We employ a continuous energy feature representation to enable the networks to organically learn the distribution relationships between different reaction energies, laying the groundwork for accessing events at untrained energies. This continuous conditional energy provides a degree of versatility to the cFAT-GAN for its further development as a significant research tool in high-energy and nuclear physics.