A Lorentz-equivariant transformer for all of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, H. Qu, Jonas Spinner, Jesse Thaler
Abstract
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures.
Topics & Concepts
Large Hadron ColliderTransformerEquivariant mapComputer sciencePhysicsAmplitudeScalingParticle physicsHomogeneous spaceArchitectureLorentz covarianceMathematicsScalabilityGenerative grammarArtificial intelligenceRange (aeronautics)Algebra over a fieldBenchmark (surveying)Nuclear electronicsLinear variable differential transformerAlgorithmData-drivenParticle physics theoretical and experimental studiesParticle Accelerators and Free-Electron LasersSuperconducting Materials and Applications