How to GAN Higher Jet Resolution
Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, J. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, D. Whiteson
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.
Topics & Concepts
Jet (fluid)Large Hadron ColliderQuantum chromodynamicsResolution (logic)PhysicsMassless particleParticle physicsHigh resolutionSimple (philosophy)Generative grammarComputer scienceArtificial intelligenceMechanicsRemote sensingGeographyEpistemologyPhilosophyParticle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchComputational Physics and Python Applications