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

• URL: http://arxiv.org/abs/2106.09474v1
• Date: Thu, 17 Jun 2021 13:24:36 GMT
• Title: Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
• Authors: Joseph Aylett-Bullock, Simon Badger, Ryan Moodie
• Abstract summary: We investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We develop a realistic simulation method that can be applied to hadron collider observables.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $2\to3$ and $2\to4$ 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.

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