  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.
