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Precision theoretical predictions for high multiplicity scattering rely onthe evaluation of increasingly complicated scattering amplitudes which comewith an extremely high CPU cost. For state-of-the-art processes this can causetechnical bottlenecks in the production of fully differential distributions. Inthis article we explore the possibility of using neural networks to approximatemulti-variable scattering amplitudes and provide efficient inputs for MonteCarlo integration. We focus on QCD corrections to $e^+e^-\to$ jets up toone-loop and up to five jets. We demonstrate reliable interpolation when aseries of networks are trained to amplitudes that have been divided intosectors defined by their infrared singularity structure. Complete simulationsfor one-loop distributions show speed improvements of at least an order ofmagnitude over a standard approach.

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes