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arXiv · 2112.09145

Targeting Multi-Loop Integrals with Neural Networks

Ramon Winterhalder·Vitaly Magerya·Emilio Villa·Stephen P. Jones·Matthias Kerner·Anja Butter·Gudrun Heinrich·Tilman Plehn
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Abstract

Numerical evaluations of Feynman integrals often proceed via a deformation of the integration contour into the complex plane. While valid contours are easy to construct, the numerical precision for a multi-loop integral can depend critically on the chosen contour. We present methods to optimize this contour using a combination of optimized, global complex shifts and a normalizing flow. They can lead to a significant gain in precision.

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Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn. 2021-12-16. Targeting Multi-Loop Integrals with Neural Networks. https://doi.org/10.21468/scipostphys.12.4.129

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