14 citations · 14 across the 4 of their papers we have counts for
4 papers
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
Justin Berman, Francois Charton, Andres Luna +2
In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent bottleneck in state-of-the-ar…
Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning
Matt von Hippel, Matthias Wilhelm
Integration-by-parts reductions of Feynman integrals pose a frequent bottle-neck in state-of-the-art calculations in theoretical particle and gravitational-wave physics, and rely o…
Recurrent Features of Amplitudes in Planar Super Yang-Mills Theory
Tianji Cai, François Charton, Kyle Cranmer +3
The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering am…
Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
Tianji Cai, Garrett W. Merz, François Charton +4
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 th…