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arXiv · 2110.13632

Generative Networks for Precision Enthusiasts

Anja Butter·Theo Heimel·Sander Hummerich·Tobias Krebs·Tilman Plehn·Armand Rousselot·Sophia Vent
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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.

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Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent. 2021-10-22. Generative Networks for Precision Enthusiasts. https://doi.org/10.21468/scipostphys.14.4.078

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