Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
This work provides a base for faster simulation in High Energy Particle Physics, addressing a domain-specific need for improved generative modeling in physics synthesis.
The paper tackled the problem of generating realistic jet images for high-energy particle physics simulations by proposing a Location-Aware Generative Adversarial Network (GAN) architecture, which produced images with pixel intensities spanning many orders of magnitude and exhibited desired physical properties like jet mass and n-subjettiness.
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.