Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Jesús Arjona Martínez, T. Q. Nguyen, M. Pierini, M. Spiropulu, Jean-Roch Vlimant
Abstract
Abstract We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.
Topics & Concepts
Event (particle physics)Generative grammarAdversarial systemParticle (ecology)Computer scienceLarge Hadron ColliderPhysicsArtificial intelligenceGeologyParticle physicsOceanographyAstrophysicsParticle physics theoretical and experimental studiesParticle Detector Development and PerformanceHigh-Energy Particle Collisions Research