GANplifying Event Samples
This addresses the need for improved statistical precision in particle physics simulations, but appears incremental as it builds on existing generative network methods.
The paper tackled the problem of whether generative networks can enhance statistical precision beyond the training sample in particle physics event generation, showing that they amplify training statistics and quantifying this with an amplification factor or equivalent event numbers.
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.