Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of simulated events. Moreover, simulation parameters have to be fine-tuned to reproduce situations in the high-energy particle interactions which is not trivial in some phase spaces in physics interests. In this paper, we suggest a new method based on the Wasserstein Generative Adversarial Network (WGAN) that can learn the probability distribution of the real data. Our method is capable of event generation at a very short computing time compared to the traditional MC generators. The trained WGAN is able to reproduce the shape of the real data with high fidelity.

高度可靠的蒙特卡洛事件发生器和检测器仿真程序对于高能物理中的精确测量非常重要。产生足够数量的模拟事件需要大量的计算资源。此外，必须对模拟参数进行微调以重现高能粒子相互作用中的情况，这在物理学上对某些相空间而言并非微不足道。在本文中，我们提出了一种基于Wasserstein生成对抗网络（WGAN）的新方法，该方法可以学习真实数据的概率分布。与传统的MC生成器相比，我们的方法能够在很短的计算时间内生成事件。训练有素的WGAN能够以高保真度再现真实数据的形状。