Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
<|reference_start|>Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory: 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.<|reference_end|>
@article{cai2024transforming,
    title={Transforming the Bootstrap: Using Transformers to Compute Scattering
  Amplitudes in Planar N = 4 Super Yang-Mills Theory},
    author={Tianji Cai, Garrett W. Merz, Franc{c}ois Charton, Niklas Nolte,
  Matthias Wilhelm, Kyle Cranmer, Lance J. Dixon},
    journal={Mach.Learn.Sci.Tech. 5 (2024) 3, 035073},
    year={2024},
    doi={10.1088/2632-2153/ad743e},
    number={SLAC-PUB-17774},
    archivePrefix={arXiv},
    eprint={2405.06107},
    primaryClass={cs.LG cs.SC hep-ph hep-th stat.ML}
}