Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

Badger, Simon; Bullock, Joseph

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: Jun 25, 2020
• Journal: Journal of High Energy Physics
• Print ISSN: 1126-6708
• Electronic ISSN: 1029-8479
• 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
• Public URL: https://durham-repository.worktribe.com/output/1267857