Year: 2024
Type: conference-paper
Abstract: 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 architecture for high-energy physics. L-GATr represents high-energy data in a geometric algebra over four-dimensional space-time and is equivariant under Lorentz transformations, the symmetry group of relativistic kinematics. At the same time, the architecture is a Transformer, which makes it versatile and scalable to large systems. L-GATr is first demonstrated on regression and classification tasks from particle physics. We then construct the first Lorentz-equivariant generative model: a continuous normalizing flow based on an L-GATr network, trained with Riemannian flow matching. Across our experiments, L-GATr is on par with or outperforms strong domain-specific baselines.
DOI: 10.52202/079017-0699
Authors: Jonas Spinner, Victor Bresó, Pim De Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: TRR 257: Phänomenologische Elementarteilchenphysik nach der HiggsEntdeckung, G2304628665, G2366492356, G2645866196, THEORETICAL HIGH ENERGY PHYSICS, AI Institute: AI Research Institute for Fundamental Interactions, G4016415828, G6526081583, G6794394390, EXC 2181: STRUKTUREN: Emergenz in Natur, Mathematik und komplexen Daten, G8202366302, G8233100948, G97843138
OpenAlex ID: W4415798409