2020/12/22 by Pierre Baldi, Baldi, Pierre, Lukas Blecher +17
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #hep-ph
paper · pdf · doi:10.48550/arxiv.2012.11944
25 pages, 11 figures; implemented SciPost reviewer comments, clarified definitions and expanded discussion in high-level observable benchmarking subsection (section 3.3 and Fig. 7)
openalex publication_date 2020/12/22 · arxiv created 2021/12/02 · arxiv updated 2021/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.