A Lorentz-equivariant transformer for all of the LHC - Brehmer, Johann; Bresó Pla, Víctor; de Haan, Pim; Plehn, Tilman; Qu, Huilin; Spinner, Jonas; Thaler, Jesse (SciPost physics, 2025)
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.
Veröffentlicht: 23. Oktober 2025
Gesehen am 04.12.2025