Kicking it off(-shell) with direct diffusion
Anja Butter, Tomáš Ježo, Michael Klasen, Mathias Kuschick, Sofia Palacios Schweitzer, Tilman Plehn
Abstract
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.
Topics & Concepts
Shell (structure)DiffusionMaterials sciencePhysicsThermodynamicsComposite materialParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle InteractionsBlack Holes and Theoretical Physics