How to GAN Event Unweighting

How to GAN Event Unweighting

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 proc...

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Bibliographic Details
Main Author: Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
Format: Article
Language:English
Published: SciPost 2021-04-01
Series:SciPost Physics
Online Access:https://scipost.org/SciPostPhys.10.4.089
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spelling doaj-8d5043cc3ca14d0b839889e9abf7f0c22021-04-23T11:46:24ZengSciPostSciPost Physics2542-46532021-04-0110408910.21468/SciPostPhys.10.4.089How to GAN Event UnweightingMathias Backes, Anja Butter, Tilman Plehn, Ramon WinterhalderEvent 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.https://scipost.org/SciPostPhys.10.4.089
collection DOAJ
language English
format Article
sources DOAJ
author Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
spellingShingle Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
How to GAN Event Unweighting
SciPost Physics
author_facet Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
author_sort Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder
title How to GAN Event Unweighting
title_short How to GAN Event Unweighting
title_full How to GAN Event Unweighting
title_fullStr How to GAN Event Unweighting
title_full_unstemmed How to GAN Event Unweighting
title_sort how to gan event unweighting
publisher SciPost
series SciPost Physics
issn 2542-4653
publishDate 2021-04-01
description 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.
url https://scipost.org/SciPostPhys.10.4.089
work_keys_str_mv AT mathiasbackesanjabuttertilmanplehnramonwinterhalder howtoganeventunweighting
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