Generative Networks for Precision Enthusiasts

Sander Hummerich, Armand Rousselot

Abstract:

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.