Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

Aylett-Bullock, Joseph; Badger, Simon; Moodie, Ryan

doi:10.1007/jhep08(2021)066

Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

Aylett-Bullock, Joseph; Badger, Simon; Moodie, Ryan

Authors

Joseph Aylett-Bullock

Simon Badger

Ryan Moodie

Abstract

Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion, and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library, and interfaced to the Sherpa Monte Carlo event generator, where we perform a detailed study for 2 → 3 and 2 → 4 scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.

Citation

Aylett-Bullock, J., Badger, S., & Moodie, R. (2021). Optimising simulations for diphoton production at hadron colliders using amplitude neural networks. Journal of High Energy Physics, 2021(8), https://doi.org/10.1007/jhep08%282021%29066

Journal Article Type	Article
Acceptance Date	Jul 22, 2021
Online Publication Date	Aug 16, 2021
Publication Date	2021
Deposit Date	Nov 9, 2021
Publicly Available Date	Nov 9, 2021
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	2021
Issue	8
DOI	https://doi.org/10.1007/jhep08%282021%29066
Public URL	https://durham-repository.worktribe.com/output/1222729

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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.