Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Jie Ren, Lei Wu, Jin Min Yang
Abstract
The top-Higgs coupling plays an important role in particle physics and cosmology. The precision measurements of this coupling can provide an insight to new physics beyond the Standard Model. In this paper, we propose to use Message Passing Neural Network (MPNN) to reveal the CP nature of top-Higgs interaction through semi-leptonic channel pp→t(→bℓ−νℓ)t¯(→b¯jj)h(→bb¯). Using the test statistics constructed from the event classification probabilities given by the MPNN, we find that the pure CP-even and CP-odd components can be well distinguished at the LHC, with at most 300 fb−1 experimental data.
Topics & Concepts
PhysicsHiggs bosonLarge Hadron ColliderParticle physicsBar (unit)Coupling (piping)Physics beyond the Standard ModelStandard Model (mathematical formulation)Artificial neural networkArtificial intelligenceComputer scienceHistoryGauge (firearms)ArchaeologyMeteorologyMechanical engineeringEngineeringParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle InteractionsHigh-Energy Particle Collisions Research