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pro vyhledávání: '"Bresó, Víctor"'
Autor:
Brehmer, Johann, Bresó, Víctor, de Haan, Pim, Plehn, Tilman, Qu, Huilin, Spinner, Jonas, Thaler, Jesse
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
Externí odkaz:
http://arxiv.org/abs/2411.00446
We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and derivative learnin
Externí odkaz:
http://arxiv.org/abs/2410.07315
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architec
Externí odkaz:
http://arxiv.org/abs/2405.14806