تقرير
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
| العنوان: | Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics |
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| المؤلفون: | Spinner, Jonas, Bresó, Victor, de Haan, Pim, Plehn, Tilman, Thaler, Jesse, Brehmer, Johann |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science High Energy Physics - Phenomenology Physics (Other) Statistics |
| مصطلحات موضوعية: | Physics - Data Analysis, Statistics and Probability, Computer Science - Machine Learning, High Energy Physics - Phenomenology, Statistics - Machine Learning |
| الوصف: | Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics. L-GATr represents high-energy data in a geometric algebra over four-dimensional space-time and is equivariant under Lorentz transformations, the symmetry group of relativistic kinematics. At the same time, the architecture is a Transformer, which makes it versatile and scalable to large systems. L-GATr is first demonstrated on regression and classification tasks from particle physics. We then construct the first Lorentz-equivariant generative model: a continuous normalizing flow based on an L-GATr network, trained with Riemannian flow matching. Across our experiments, L-GATr is on par with or outperforms strong domain-specific baselines. Comment: 10+12 pages, 5+2 figures, 2 tables, v2: Extend acknowledgements, add link to github repo |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.14806 |
| رقم الأكسشن: | edsarx.2405.14806 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |