Lorentz-equivariance without limitations
Lorentz Local Canonicalization (LLoCa) provides exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. It is applied to the LHC, predicting local reference frames for each particle and propagating any-order tensorial information between them, and has been tested on graph networks and transformers. The method demonstrates cutting‑edge performance on amplitude regression, end‑to‑end event generation, and jet tagging, and a large top tagging dataset was introduced to benchmark LLoCa versions of various benchmark architectures, highlighting the importance of…
Lorentz Local Canonicalization (LLoCa) provides exact Lorentz-equivariance for arbitrary neural networks with minimal computational overhead. It is applied to the LHC, predicting local reference frames for each particle and propagating any-order tensorial information between them, and has been tested on graph networks and transformers. The method demonstrates cutting‑edge performance on amplitude regression, end‑to‑end event generation, and jet tagging, and a large top tagging dataset was introduced to benchmark LLoCa versions of various benchmark architectures, highlighting the importance of…
Swiss AI can benefit from LLoCa’s efficient symmetry handling for physics‑informed models.
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