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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ArXiv ePrint: 2002.07516
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Badger, S., Bullock, J. Using neural networks for efficient evaluation of high multiplicity scattering amplitudes. J. High Energ. Phys. 2020, 114 (2020). https://doi.org/10.1007/JHEP06(2020)114
- NLO Computations
- QCD Phenomenology