Lorentz Local Canonicalization (LLoCa) ensures exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. For the LHC, it equivariantly predicts local reference frames for each particle and propagates any-order tensorial information between them. We apply it to graph networks and transformers. We showcase its cutting-edge performance on amplitude regression, end-to-end event generation, and jet tagging. For jet tagging, we introduce a large top tagging dataset to benchmark LLoCa versions of a range of established benchmark architectures and highlight the importance of symmetry breaking.
Cite
@article{arxiv.2508.14898,
title = {Lorentz-Equivariance without Limitations},
author = {Luigi Favaro and Gerrit Gerhartz and Fred A. Hamprecht and Peter Lippmann and Sebastian Pitz and Tilman Plehn and Huilin Qu and Jonas Spinner},
journal= {arXiv preprint arXiv:2508.14898},
year = {2025}
}
Comments
33 pages, 9 figures, 8 tables. v2: refined and additional results, added appendix B and C, 38 pages, 9 figures, 10 tables