Fast and accurate simulation of particle detectors using generative adversarial networks

TL;DR

This paper demonstrates that generative adversarial networks can simulate particle-detector responses to hadronic jets with high accuracy and significantly faster than traditional methods.

Contribution

The work introduces a novel application of GANs for particle detector simulation, achieving high fidelity and speed improvements over existing algorithms.

Findings

GANs achieve high-fidelity simulation of detector responses.

Simulation speed increases by several orders of magnitude.

Potential for real-time particle detector modeling.

Abstract

Deep generative models parametrised by neural networks have recently started to provide accurate results in modelling natural images. In particular, generative adversarial networks provide an unsupervised solution to this problem. In this work we apply this kind of technique to the simulation of particle-detector response to hadronic jets. We show that deep neural networks can achieve high-fidelity in this task, while attaining a speed increase of several orders of magnitude with respect to traditional algorithms.