{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lorentz-local-canonicalization-how-to-make","title":"Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant","arxiv_id":"2505.20280","date":"2025-05-26","proceeding":null,"authors":["Jonas Spinner","Luigi Favaro","Peter Lippmann","Sebastian Pitz","Gerrit Gerhartz","Tilman Plehn","Fred A. Hamprecht"],"abstract":"Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly Lorentz-equivariant. Using equivariantly predicted local reference frames, we construct LLoCa-transformers and graph networks. We adapt a recent approach to geometric message passing to the non-compact Lorentz group, allowing propagation of space-time tensorial features. Data augmentation emerges from LLoCa as a special choice of reference frame. 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