A Lorentz-Equivariant Transformer for All of the LHC

Autor:	Brehmer, Johann, Bresó, Víctor, de Haan, Pim, Plehn, Tilman, Qu, Huilin, Spinner, Jonas, Thaler, Jesse
Rok vydání:	2024
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment
Druh dokumentu:	Working Paper
Popis:	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. Comment: 26 pages, 7 figures, 8 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.00446 Zobrazit plný text záznamu View this record from Arxiv