# Targeting Multi-Loop Integrals with Neural Networks

### 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