How to GAN higher jet resolution - Baldi, Pierre; Blecher, Lukas; Butter, Anja; Collado, Julian; Howard, Jessica N.; Keilbach, Fabian; Plehn, Tilman; Kasieczka, Gregor; Whiteson, Daniel (Arxiv, 2020)
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.
Identifizierung der Ressource nach: December 3, 2021
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