Transforming the bootstrap: using Transformers to compute scattering amplitudes in planar N = 4 Super Yang-Mills theory
Abstract We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to predict these coefficients. The problem can be formulated in a language-like representation amenable to standard cross-entropy training objectives. We design two related experiments and show that the model achieves high accuracy (> 98%) on both tasks. Our work shows that Transformers can be applied successfully to problems in theoretical physics that require exact solutions.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2438115
- Journal Information:
- Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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