Optimising hadronic collider simulations using amplitude neural networks. (arXiv:2202.04506v2 [hep-ph] UPDATED)

Precision phenomenological studies of high-multiplicity scattering processes
at collider experiments present a substantial theoretical challenge and are
vitally important ingredients in experimental measurements. Machine learning
technology has the potential to dramatically optimise simulations for
complicated final states. We investigate the use of neural networks to
approximate matrix elements, studying the case of loop-induced diphoton
production through gluon fusion. We train neural network models on one-loop
amplitudes from the NJet C++ library and interface them with the Sherpa Monte
Carlo event …

amplitude arxiv networks neural networks simulations