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arXiv · 2508.14898

Lorentz-Equivariance without Limitations

Luigi Favaro·Gerrit Gerhartz·Fred A. Hamprecht·Peter Lippmann·Sebastian Pitz·Tilman Plehn·Huilin Qu·Jonas Spinner
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Abstract

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.

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Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht, Peter Lippmann, Sebastian Pitz, Tilman Plehn, Huilin Qu, Jonas Spinner. 2025-08-20. Lorentz-Equivariance without Limitations. https://arxiv.org/abs/2508.14898

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