Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

Program Document · Mon May 13 04:00:00 EDT 2024

OSTI ID:2349325

Lance, Dixon ^[1]

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1. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States), FPD Theory Elem Particle Phys

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.

• Research Organization: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States), FPD Theory Elem Particle Phys
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC02-76SF00515
• OSTI ID: 2349325
• Report Number(s): SLAC-PUB-17774
• Country of Publication: United States
• Language: English