Optimising hadronic collider simulations using amplitude neural networks

TL;DR

This paper demonstrates that neural networks can effectively approximate complex matrix elements in collider simulations, significantly reducing computation time while maintaining accuracy.

Contribution

It introduces a neural network approach to approximate loop-induced matrix elements, integrated with a collider simulation framework, improving efficiency for high-multiplicity processes.

Findings

Neural networks accurately reproduce matrix element distributions.

Simulation time reduced by a factor of thirty.

Method compatible with existing collider simulation tools.

Abstract

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 generator to provide the matrix element within a realistic hadronic collider simulation. Computing some standard observables with the models and comparing to conventional techniques, we find excellent agreement in the distributions and a reduced total simulation time by a factor of thirty.