Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
This work addresses simulation efficiency for high-energy physics experiments, but it is incremental as it builds on prior neural network approaches for scattering processes.
The authors tackled the problem of optimizing event generation for diphoton production at hadron colliders by using neural networks to approximate matrix elements, achieving a realistic simulation method for 2→3 and 2→4 scattering processes with detailed reliability studies.
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.