The MadNIS reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder
Abstract
In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators.
Topics & Concepts
Computer scienceLarge Hadron ColliderGenerator (circuit theory)Event (particle physics)Series (stratigraphy)Particle physicsPhysicsPaleontologyBiologyQuantum mechanicsPower (physics)Particle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchDistributed and Parallel Computing Systems