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.

How to GAN Event Unweighting / M. Backes, A. Butter, T. Plehn, R. Winterhalder. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 10:4(2021 Apr 23), pp. 089.1-089.15. [10.21468/SciPostPhys.10.4.089]

How to GAN Event Unweighting

R. Winterhalder
Ultimo
2021

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.
Settore PHYS-02/A - Fisica teorica delle interazioni fondamentali, modelli, metodi matematici e applicazioni
23-apr-2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1173892
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