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.
@article{arxiv.2106.09474,
title = {Optimising simulations for diphoton production at hadron colliders using amplitude neural networks},
author = {Joseph Aylett-Bullock and Simon Badger and Ryan Moodie},
journal= {arXiv preprint arXiv:2106.09474},
year = {2021}
}