Deep learning exotic hadrons
L. Ng, Łukasz Bibrzycki, Jannes Nys, C. Fernández-Ramírez, A. Pilloni, V. Mathieu, A. J. Rasmusson, Adam P. Szczepaniak
Abstract
We perform the first amplitude analysis of experimental data using deep neural networks to determine the nature of an exotic hadron. Specifically, we study the line shape of the ${P}_{c}(4312)$ signal reported by the LHCb collaboration, and we find that its most likely interpretation is that of a virtual state. This method can be applied to other near-threshold resonance candidates.
Topics & Concepts
HadronExotic hadronParticle physicsSIGNAL (programming language)Interpretation (philosophy)AmplitudeResonance (particle physics)Deep learningLine (geometry)Deep neural networksComputer scienceArtificial intelligencePhysicsNuclear physicsMathematicsOpticsGeometryProgramming languageParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle InteractionsHigh-Energy Particle Collisions Research