GANplifying Event Samples

Author(s): Butter, Anja; Diefenbacher, Sascha; Kasieczka, Gregor; Nachman, Benjamin; Plehn, Tilman | Abstract: A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.