# Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

## TL;DR

This paper introduces a location-aware GAN architecture that generates realistic jet images for high energy physics, bridging machine learning and physical process simulation with promising results.

## Contribution

The paper presents a novel GAN architecture tailored for physics simulation, enabling realistic jet image generation with physical property preservation.

## Key findings

- GAN-generated images span many orders of magnitude in pixel intensities.
- Generated images exhibit key physical properties like jet mass and n-subjettiness.
- The approach offers a promising method for faster physics simulations.

## Abstract

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.