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.
翻译:机器学习技术有可能极大地优化事件生成和模拟。我们继续调查神经网络用于为高多重散射过程估计矩阵元素的使用情况。我们侧重于通过格鲁翁聚变循环诱发的二磷生产案例,并开发一种现实的模拟方法,可用于对可观测到的电相对撞器。神经网络通过NJet C++图书馆实施的单行振幅来接受培训,并与Sherpa Monte Carlo事件生成器接口,我们在那里进行一项2美元至3美元和2美元至4美元的散射问题的详细研究。我们还考虑了在改变对阶段空间和神经网络模拟可靠性的动态切除作用时,受过训练的网络如何运作。