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.
@article{arxiv.2008.06545,
title = {GANplifying Event Samples},
author = {Anja Butter and Sascha Diefenbacher and Gregor Kasieczka and Benjamin Nachman and Tilman Plehn},
journal= {arXiv preprint arXiv:2008.06545},
year = {2021}
}