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Generative Networks for Precision Enthusiasts
by Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent
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Submission summary
| Authors (as registered SciPost users): | Theo Heimel · Tilman Plehn · Armand Rousselot |
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| Preprint Link: | https://arxiv.org/abs/2110.13632v3 (pdf) |
| Date accepted: | 2023-02-09 |
| Date submitted: | 2022-12-20 10:47 |
| Submitted by: | Heimel, Theo |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
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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.
List of changes
See responses to the referees for a detailed list of changes.
Published as SciPost Phys. 14, 078 (2023)