Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network

TL;DR

This paper introduces a Wasserstein GAN-based method for ultra-fast, accurate simulation of electromagnetic calorimeter showers, matching GEANT4 simulation quality while significantly reducing computational time.

Contribution

The paper presents a novel Wasserstein GAN approach for simulating calorimeter showers, incorporating energy and position dependencies, achieving realistic results comparable to GEANT4.

Findings

Generated showers match GEANT4 in key observables

Method significantly reduces simulation time

Captures fluctuations and correlations accurately

Abstract

Simulations of particle showers in calorimeters are computationally time-consuming, as they have to reproduce both energy depositions and their considerable fluctuations. A new approach to ultra-fast simulations are generative models where all calorimeter energy depositions are generated simultaneously. We use GEANT4 simulations of an electron beam impinging on a multi-layer electromagnetic calorimeter for adversarial training of a generator network and a critic network guided by the Wasserstein distance. The generator is constraint during the training such that the generated showers show the expected dependency on the initial energy and the impact position. It produces realistic calorimeter energy depositions, fluctuations and correlations which we demonstrate in distributions of typical calorimeter observables. In most aspects, we observe that generated calorimeter showers reach the level of showers as simulated with the GEANT4 program.