English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning technology has the potential to dramatically optimise eventgeneration and simulations. We continue to investigate the use of neuralnetworks to approximate matrix elements for high-multiplicity scatteringprocesses. We focus on the case of loop-induced diphoton production throughgluon fusion and develop a realistic simulation method that can be applied tohadron collider observables. Neural networks are trained using the one-loopamplitudes implemented in the NJet C++ library and interfaced to the SherpaMonte Carlo event generator where we perform a detailed study for $2\to3$ and$2\to4$ scattering problems. We also consider how the trained networks performwhen varying the kinematic cuts effecting the phase space and the reliabilityof the neural network simulations.

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