How to GAN Event Unweighting
Mathias Backes
2021Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano)60 citationsDOIOpen Access PDF
Abstract
Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.
Topics & Concepts
Large Hadron ColliderBottleneckEvent (particle physics)Computer scienceSimulationEmbedded systemPhysicsParticle physicsQuantum mechanicsParticle physics theoretical and experimental studiesComputational Physics and Python ApplicationsDistributed and Parallel Computing Systems