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Optimising Simulations for Diphoton Production at Hadron Colliders Using Amplitude Neural Networks by Joseph Aylett-Bullock; Simon Badger; Ryan Moodie is a Physics and Astronomy article available to read on EtoBox.

What is Optimising Simulations for Diphoton Production at Hadron Colliders Using Amplitude Neural Networks about?

## 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.

Who reads Optimising Simulations for Diphoton Production at Hadron Colliders Using Amplitude Neural Networks?

Author

Joseph Aylett-Bullock; Simon Badger; Ryan Moodie

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Physics and Astronomy (Physical Sciences)