Simulations of particle showers in calorimeters are computationally
time-consuming, as they have to reproduce both energy depositions and their
considerable fluctuations. A new approach to ultra-fast simulations are
generative models where all calorimeter energy depositions are generated
simultaneously. We use GEANT4 simulations of an electron beam impinging on a
multi-layer electromagnetic calorimeter for adversarial training of a generator
network and a critic network guided by the Wasserstein distance. The generator
is constraint during the training such that the generated showers show the
expected dependency on the initial energy and the impact position. It produces
realistic calorimeter energy depositions, fluctuations and correlations which
we demonstrate in distributions of typical calorimeter observables. In most
aspects, we observe that generated calorimeter showers reach the level of
showers as simulated with the GEANT4 program.

本文提出了基于生成式模型的高效粒子淋浴模拟方法，并使用对抗训练的方式训练生成器和评论者网络以生成与 GEANT4 程序模拟类似的粒子淋浴，从而在大多数方面达到了实现高质量粒子淋浴模拟的预期目标。