Fast and accurate simulation of particle detectors using generative adversarial networks
High Energy Physics - Experiment
2018-11-27 v2 High Energy Physics - Phenomenology
Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.1805.00850,
title = {Fast and accurate simulation of particle detectors using generative adversarial networks},
author = {Pasquale Musella and Francesco Pandolfi},
journal= {arXiv preprint arXiv:1805.00850},
year = {2018}
}
Comments
Published on Comp. Soft. for Big Sci