Heidelberg 2022 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
T: Fachverband Teilchenphysik
T 2: QCD (Theorie) 1
T 2.7: Vortrag
Montag, 21. März 2022, 17:45–18:00, T-H15
Targeting Multi-Loop Integrals with Neural Networks — •Ramon Winterhalder1,2,3, Vitaly Magerya4, Emilio Villa4, Stephen P. Jones5, Matthias Kerner4,6,7, Anja Butter1,2, Gudrun Heinrich2,4, and Tilman Plehn1,2 — 1Institut für Theoretische Physik, Universität Heidelberg, Germany — 2HEiKA - Heidelberg Karlsruhe Strategic Partnership, Heidelberg University, Karlsruhe Institute of Technology (KIT), Germany — 3Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université catholique de Louvain, Belgium — 4Institut für Theoretische Physik, Karlsruher Institut für Technologie, Germany — 5Institute for Particle Physics Phenomenology, Durham University, UK — 6Institut für Astroteilchenphysik, Karlsruher Institut für Technologie, Germany — 7Physik-Institut, Universität Zürich, Switzerland
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 reached for a multi-loop integral can depend critically on the chosen contour. We present methods to optimize this contour using a combination of machine-learned complex shifts and a normalizing flow. This can, potentially, lead to a very significant gain in precision.