We provide a bridge between generative modeling in the Machine Learning
community and simulated physical processes in High Energy Particle Physics by
applying a novel Generative Adversarial Network (GAN) architecture to the
production of jet images -- 2D representations of energy depositions from
particles interacting with a calorimeter. We propose a simple architecture, the
Location-Aware Generative Adversarial Network, that learns to produce realistic
radiation patterns from simulated high energy particle collisions. The pixel
intensities of GAN-generated images faithfully span over many orders of
magnitude and exhibit the desired low-dimensional physical properties (i.e.,
jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a
novel empirical validation of image quality and validity of GAN-produced
simulations of the natural world. This work provides a base for further
explorations of GANs for use in faster simulation in High Energy Particle
Physics.

我们通过应用一种新颖的生成对抗网络（GAN）架构到喷气图像的生成，提供了机器学习社区中生成建模与高能粒子物理中模拟物理过程之间的桥梁，并提出了一种简单的位置感知生成对抗网络。这项工作为将 GAN 用于高能粒子物理中的快速模拟提供了基础。