Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations

TL;DR

This paper introduces a new neural network architecture using 2D convolutions for simulating particle detector responses, achieving higher accuracy and efficiency than previous 3D convolutional GANs in high energy physics calorimeter simulations.

Contribution

The paper presents a novel 2D convolutional GAN architecture that improves accuracy and reduces computational resources compared to earlier 3D convolutional models for calorimeter simulation.

Findings

The new architecture outperforms previous models in accuracy.

It requires less computational resources.

It effectively reproduces detector response simulations.

Abstract

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has motivated the investigation of different, faster approaches for replacing the standard Monte Carlo simulations. Deep Learning Generative Adversarial Networks are among the most promising alternatives. Previous studies showed that they achieve the necessary level of accuracy while decreasing the simulation time by orders of magnitudes. In this paper we present a newly developed neural network architecture which reproduces a three-dimensional problem employing 2D convolutional layers and we compare its performance with an earlier architecture consisting of 3D convolutional layers. The performance evaluation relies on direct comparison to Monte Carlo simulations, in terms of different physics quantities usually employed to quantify the detector response. We prove that our new neural network architecture reaches a higher level of accuracy with respect to the 3D convolutional GAN while reducing the necessary computational resources. Calorimeters are among the most expensive detectors in terms of simulation time. Therefore we focus our study on an electromagnetic calorimeter prototype with a regular highly granular geometry, as an example of future calorimeters.