Invertible networks or partons to detector and back again
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich Köthe
Abstract
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.
Topics & Concepts
DetectorPartonPhysicsInvertible matrixProbabilistic logicQuantum chromodynamicsParticle physicsInverseInterpretation (philosophy)Statistical physicsStatistical modelVariable (mathematics)Phase (matter)AlgorithmProduction (economics)Particle detectorArtificial neural networkMathematicsStatistical fluctuationsBayesian probabilityComputer scienceTheoretical physicsMultiplicative functionParticle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchQuantum Chromodynamics and Particle Interactions