Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

TL;DR

This paper develops and validates novel 2D convolutional neural network architectures using GANs to significantly accelerate high energy physics detector simulations while maintaining high accuracy.

Contribution

It introduces new 2D convolutional GAN architectures that outperform previous 3D models in speed and accuracy for particle detector simulation tasks.

Findings

2D GANs achieve comparable physics accuracy to 3D models

Simulation speed is increased by orders of magnitude

Models outperform previous architectures in accuracy and efficiency

Abstract

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo approach. We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.