Precision-machine learning for the matrix element method
Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter
Abstract
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.
Topics & Concepts
Yukawa potentialLarge Hadron ColliderComputer scienceInferenceTransfer of learningElement (criminal law)Matrix (chemical analysis)TransformerHiggs bosonPhase spaceParticle physicsCoupling (piping)Theoretical computer scienceArtificial intelligenceAlgorithmPhysicsEngineeringMechanical engineeringElectrical engineeringMaterials scienceVoltageLawThermodynamicsPolitical scienceComposite materialParticle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchQuantum Chromodynamics and Particle Interactions