SciPost Physics Core· 2026Q1
Lorentz-equivariance without limitations
- 3citations
- Q1SCImago
- 2026year
Short summary
Lorentz Local Canonicalization (LLoCa) achieves exact Lorentz-equivariance in neural networks with minimal overhead, enabling precise prediction of particle reference frames and tensorial information propagation for LHC applications.
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Key points
- LLoCa guarantees exact Lorentz-equivariance for any neural network with low computational overhead.
- It enables equivariant prediction of local particle reference frames and tensorial information propagation.
- LLoCa shows state-of-the-art performance on amplitude regression, event generation, and jet tagging.
- A new large top tagging dataset is presented to benchmark LLoCa on various architectures.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. SciPost Physics Core, 2026 · DOI ↗
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Field: Astronomy and Astrophysics
Astronomy and AstrophysicsPhysics and Astronomy