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How to GAN higher jet resolution

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QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduc...

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Main Authors: Baldi, Pierre (Author) , Blecher, Lukas (Author) , Butter, Anja (Author) , Collado, Julian (Author) , Howard, Jessica N. (Author) , Keilbach, Fabian (Author) , Plehn, Tilman (Author) , Kasieczka, Gregor (Author) , Whiteson, Daniel (Author)
Format: Article (Journal)
Language:English
Published: 23 September 2022
In: SciPost physics
Year: 2022, Volume: 13, Issue: 3, Pages: 1-22
ISSN:2542-4653
DOI:10.21468/SciPostPhys.13.3.064
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.13.3.064
Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.13.3.064
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Author Notes:Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka and Daniel Whiteson
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Summary:QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim network architecture can be constructed once we have control of the full network performance.
Item Description:Im PDF ist irrtümlich ein falsches Erscheinungsdatum angegeben: 23-09-2023
Gesehen am 15.11.2023
Physical Description:Online Resource
ISSN:2542-4653
DOI:10.21468/SciPostPhys.13.3.064

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