Generative Networks for Precision Enthusiasts
This work addresses the need for fast and accurate event generation in particle physics, offering incremental improvements in precision and uncertainty estimation for domain-specific applications.
The paper tackled the problem of achieving high-precision event generation for the LHC using generative networks, resulting in percent-level precision for kinematic distributions through a novel joint training method with a discriminator.
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.