Loop amplitudes from precision networks
Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn
Abstract
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.
Topics & Concepts
AmplitudeInterpolation (computer graphics)Computer scienceBayesian probabilityBayesian networkEvent (particle physics)Loop (graph theory)Phase (matter)Artificial intelligenceMachine learningAlgorithmPhysicsMathematicsOpticsCombinatoricsQuantum mechanicsMotion (physics)Particle physics theoretical and experimental studiesComputational Physics and Python ApplicationsReservoir Engineering and Simulation Methods