How to GAN higher jet resolution
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...
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
23 September 2022
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| In: |
SciPost physics
Year: 2022, Jahrgang: 13, Heft: 3, Pages: 1-22 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.13.3.064 |
| Online-Zugang: | 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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| Verfasserangaben: | Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka and Daniel Whiteson |
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| 520 | |a 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. | ||
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