Targeting multi-loop integrals with neural networks

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

doi:10.21468/SciPostPhys.12.4.129

SciPost Physics

Targeting multi-loop integrals with neural networks

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

SciPost Phys. 12, 129 (2022) · published 13 April 2022

doi: 10.21468/SciPostPhys.12.4.129
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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.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.12.4.129
TI  - Targeting multi-loop integrals with neural networks
PY  - 2022/04/13
UR  - https://scipost.org/SciPostPhys.12.4.129
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 12
IS  - 4
SP  - 129
A1  - Winterhalder, Ramon
AU  - Magerya, Vitaly
AU  - Villa, Emilio
AU  - Jones, Stephen
AU  - Kerner, Matthias
AU  - Butter, Anja
AU  - Heinrich, Gudrun
AU  - Plehn, Tilman
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.12.4.129,
	title={{Targeting multi-loop integrals with neural networks}},
	author={Ramon Winterhalder and Vitaly Magerya and Emilio Villa and Stephen P. Jones and Matthias Kerner and Anja Butter and Gudrun Heinrich and Tilman Plehn},
	journal={SciPost Phys.},
	volume={12},
	pages={129},
	year={2022},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.12.4.129},
	url={https://scipost.org/10.21468/SciPostPhys.12.4.129},
}

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Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.

¹ ² Ramon Winterhalder,
³ Vitaly Magerya,
³ Emilio Villa,
⁴ Stephen Jones,
³ Matthias Kerner,
¹ Anja Butter,
³ Gudrun Heinrich,
¹ Tilman Plehn

Funders for the research work leading to this publication

Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
Fonds De La Recherche Scientifique - FNRS (FNRS) (through Organization: Fonds National de la Recherche Scientifique [FNRS])