Targeting multi-loop integrals with neural networks
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 t...
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| Format: | Article |
| Language: | English |
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SciPost
2022-04-01
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| Series: | SciPost Physics |
| Online Access: | https://scipost.org/SciPostPhys.12.4.129 |
| _version_ | 1818059716552556544 |
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| author | Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn |
| author_facet | Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn |
| author_sort | Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T13:20:57Z |
| format | Article |
| id | doaj.art-3e9c7d6e625649ee9ac416887bbfda0c |
| institution | Directory Open Access Journal |
| issn | 2542-4653 |
| language | English |
| last_indexed | 2024-12-10T13:20:57Z |
| publishDate | 2022-04-01 |
| publisher | SciPost |
| record_format | Article |
| series | SciPost Physics |
| spelling | doaj.art-3e9c7d6e625649ee9ac416887bbfda0c2022-12-22T01:47:20ZengSciPostSciPost Physics2542-46532022-04-0112412910.21468/SciPostPhys.12.4.129Targeting multi-loop integrals with neural networksRamon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman PlehnNumerical 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.https://scipost.org/SciPostPhys.12.4.129 |
| spellingShingle | Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones, Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn Targeting multi-loop integrals with neural networks SciPost Physics |
| title | Targeting multi-loop integrals with neural networks |
| title_full | Targeting multi-loop integrals with neural networks |
| title_fullStr | Targeting multi-loop integrals with neural networks |
| title_full_unstemmed | Targeting multi-loop integrals with neural networks |
| title_short | Targeting multi-loop integrals with neural networks |
| title_sort | targeting multi loop integrals with neural networks |
| url | https://scipost.org/SciPostPhys.12.4.129 |
| work_keys_str_mv | AT ramonwinterhaldervitalymageryaemiliovillastephenpjonesmatthiaskerneranjabuttergudrunheinrichtilmanplehn targetingmultiloopintegralswithneuralnetworks |