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Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Badger, Simon; Bullock, Joseph

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes Thumbnail


Authors

Joseph Bullock



Abstract

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.

Citation

Badger, S., & Bullock, J. (2020). Using neural networks for efficient evaluation of high multiplicity scattering amplitudes. Journal of High Energy Physics, 2020(6), Article 114. https://doi.org/10.1007/jhep06%282020%29114

Journal Article Type Article
Acceptance Date May 26, 2020
Online Publication Date Jun 17, 2020
Publication Date Jun 30, 2020
Deposit Date Jun 25, 2020
Publicly Available Date Mar 28, 2024
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2020
Issue 6
Article Number 114
DOI https://doi.org/10.1007/jhep06%282020%29114

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.





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