How to GAN away detector effects
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Ramon Winterhalder
Abstract
LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.
Topics & Concepts
DetectorEvent (particle physics)Monte Carlo methodPhysicsGenerative grammarGenerative modelAlgorithmComputer scienceStatistical physicsDimension (graph theory)Key (lock)Computational physicsMathematicsPartonOpticsExperimental dataArtificial intelligenceConditional probabilityTraining setParticle physics theoretical and experimental studiesHigh-Energy Particle Collisions ResearchQuantum Chromodynamics and Particle Interactions