SciPost Submission Page
Lorentz-Equivariance without Limitations
by Luigi Favaro, Gerrit Gerhartz, Fred A. Hamprecht, Peter Lippmann, Sebastian Pitz, Tilman Plehn, Huilin Qu, Jonas Spinner
Submission summary
| Authors (as registered SciPost users): | Luigi Favaro · Tilman Plehn · Jonas Spinner |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2508.14898v2 (pdf) |
| Code repository: | https://github.com/heidelberg-hepml/lloca |
| Data repository: | https://github.com/heidelberg-hepml/lloca-experiments |
| Date submitted: | Nov. 11, 2025, 8:53 p.m. |
| Submitted by: | Luigi Favaro |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
The usage of large language models is limited to formatting and writing improvements.
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.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block