A Lorentz-equivariant transformer for all of the LHC
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The...
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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
23 October 2025
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| In: |
SciPost physics
Year: 2025, Jahrgang: 19, Heft: 4, Pages: 1-30 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.19.4.108 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.19.4.108 Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.19.4.108 
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| Verfasserangaben: | Johann Brehmer, Victor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner and Jesse Thaler |
MARC
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| 520 | |a We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures. | ||
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