Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. In particular, there exists a demand for a fast simulation of the neutron Zero Degree Calorimeter, where existing Monte Carlo-based methods impose a significant computational burden. We propose an alternative approach to the problem that leverages machine learning. Our solution utilises neural network classifiers and generative models to directly simulate the response of the calorimeter. In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step. Our approach increases the simulation speed by 2 orders of magnitude while maintaining the high fidelity of the simulation.

翻译：目前，CERN GRID超过一半的计算能力用于运行高能物理模拟。大型强子对撞机（LHC）的最新升级催生了对更高效模拟方法的需求。特别是，在零度中子量热器的快速模拟方面，现有的基于蒙特卡洛的方法带来了显著的计算负担。我们提出了一种利用机器学习的替代解决方案。该方法使用神经网络分类器和生成模型直接模拟量热器的响应。我们具体研究了变分自编码器和生成对抗网络的性能，并通过额外的正则化网络及简单有效的后处理步骤扩展了GAN架构。我们的方法在保持模拟高保真度的同时，将模拟速度提升了两个数量级。