Tuesday, 22 November 2022 at 11:15, on Zoom
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training. Orthogonal to approximation techniques we can use networks within numerical evaluations of Feynman integrals, which 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. Combining global complex shifts and a normalizing flow we can optimize the chosen contour.