Speaker
Lukas Blecher
(Universität Heidelberg)
Description
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.
| Affiliation | Heidelberg University |
|---|---|
| Academic Rank | Master Student |
Primary authors
Anja Butter
Daniel Whiteson
(University of California Irvine (US))
Gregor Kasieczka
(Hamburg University (DE))
Jessica Nicole Howard
(University of California Irvine (US))
Lukas Blecher
(Universität Heidelberg)
Pierre Baldi
(UCI)
Tilman Plehn
