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 distribu...

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Bibliographic Details
Published in:arXiv.org 2020-02
Main Authors: Badger, Simon, Bullock, Joseph
Format: Article
Language:English
Subjects:
Amplitudes
Central processing units
Computer simulation
CPUs
Interpolation
Neural networks
Scattering
Online Access:Get full text
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Summary: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 \(e^+e^-\to\) 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2002.07516