Neural Network NJet (n3jet) is a package to enable training and inference of neural networks using the NJet numerical matrix element calculation package. The package presented here accompanies the paper arXiv:2002.07516. Using neural networks for efficient evaluation of high multiplicity scattering amplitudes. Precision theoretical predictions for high multiplicity scattering rely on the evaluation of increasingly complicated scattering amplitudes which come with an extremely high CPU cost. For state-of-the-art processes this can cause technical bottlenecks in the production of fully differential distributions. In this article we explore the possibility of using neural networks to approximate multi-variable scattering amplitudes and provide efficient inputs for Monte Carlo integration. We focus on QCD corrections to e+e−→ jets up to one-loop and up to five jets. We demonstrate reliable interpolation when a series of networks are trained to amplitudes that have been divided into sectors defined by their infrared singularity structure. Complete simulations for one-loop distributions show speed improvements of at least an order of magnitude over a standard approach.
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References in zbMATH (referenced in 1 article , 1 standard article )
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- Simon Badger, Joseph Bullock: Using neural networks for efficient evaluation of high multiplicity scattering amplitudes (2020) arXiv