How to GAN Event Unweighting

Abstract

Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.

arXiv comment17 pages, 5 figures

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Cited by in corpus (5)

• Understanding Event-Generation Networks via Uncertainties
• Advancing Tools for Simulation-Based Inference
• Extrapolating Jet Radiation with Autoregressive Transformers
• The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC
• Speeding up MadGraph5_aMC@NLO