Generative networks for precision enthusiasts - Butter, Anja; Heimel, Theo; Hummerich, Sander; Krebs, Tobias; Plehn, Tilman; Rousselot, Armand; Vent, Sophia (Arxiv, 2021)
Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.
Gesehen am 15.09.2022