Jet flavour classification using DeepJet
E. S. Bols, J. Kieseler, M. Verzetti, M. Stoye, A. Stakia
Abstract
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern deep learning techniques. This new model, called DeepJet, overcomes the limitations in input size that affected previous approaches. As a result, the heavy flavour classification performance improves, and the model is extended to also perform quark-gluon tagging.
Topics & Concepts
FlavourLarge Hadron ColliderJet (fluid)ExploitTask (project management)Particle physicsRange (aeronautics)Computer scienceQuarkArchitectureArtificial intelligencePhysicsSystems engineeringAerospace engineeringEngineeringMechanicsArtVisual artsComputer securityParticle physics theoretical and experimental studiesParticle Detector Development and PerformanceHigh-Energy Particle Collisions Research