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

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

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