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Targeting Multi-Loop Integrals with Neural Networks
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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.
Forward citations
Cited by 4 Pith papers
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Agentic Re-Casting using Agentic Re-Simulations
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