How to GAN Higher Jet Resolution

Pierre Baldi; Lukas Blecher; Anja Butter; Julian Collado; Jessica N. Howard; Fabian Keilbach; Tilman Plehn; Gregor Kasieczka; Daniel Whiteson

arXiv · 2012.11944

How to GAN Higher Jet Resolution

Pierre Baldi·Lukas Blecher·Anja Butter·Julian Collado·Jessica N. Howard·Fabian Keilbach·Tilman Plehn·Gregor Kasieczka·Daniel Whiteson

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Abstract

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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Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson. 2020-12-22. How to GAN Higher Jet Resolution. https://arxiv.org/abs/2012.11944

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