A Lorentz-Equivariant Transformer for All of the LHC
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917048276221952 |
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| author | Brehmer, Johann Bresó, Víctor de Haan, Pim Plehn, Tilman Qu, Huilin Spinner, Jonas Thaler, Jesse |
| author_facet | Brehmer, Johann Bresó, Víctor de Haan, Pim Plehn, Tilman Qu, Huilin Spinner, Jonas Thaler, Jesse |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_00446 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Lorentz-Equivariant Transformer for All of the LHC Brehmer, Johann Bresó, Víctor de Haan, Pim Plehn, Tilman Qu, Huilin Spinner, Jonas Thaler, Jesse High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment 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. |
| title | A Lorentz-Equivariant Transformer for All of the LHC |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2411.00446 |