2025/05/26 by Jonas Spinner, Luigi Favaro, Spinner, Jonas +11 · 2 voices · 8 citations
#stat.ML #cs.LG #hep-ph
paper · pdf · doi:10.48550/arxiv.2505.20280
Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly Lorentz-equivariant. Using equivariantly predicted local reference frames, we construct LLoCa-transformers and graph networks. We adapt a recent approach for geometric message passing to the non-compact Lorentz group, allowing propagation of space-time tensorial features. Data augmentation emerges from LLoCa as a special choice of reference frame. Our models achieve competitive and state-of-the-art accuracy on relevant particle physics tasks, while being 4× faster and using 10× fewer FLOPs.