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Targeting Multi-Loop Integrals with Neural Networks

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

Submission summary

As Contributors: Tilman Plehn · Ramon Winterhalder
Arxiv Link: https://arxiv.org/abs/2112.09145v1 (pdf)
Date submitted: 2022-01-03 13:03
Submitted by: Winterhalder, Ramon
Submitted to: SciPost Physics
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approaches: Theoretical, Computational

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.

Current status:
Editor-in-charge assigned


Submission & Refereeing History

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Submission 2112.09145v1 on 3 January 2022

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