  We study how to use Deep Variational Autoencoders for a fast simulation of
jets of particles at the LHC. We represent jets as a list of constituents,
characterized by their momenta. Starting from a simulation of the jet before
detector effects, we train a Deep Variational Autoencoder to return the
corresponding list of constituents after detection. Doing so, we bypass both
the time-consuming detector simulation and the collision reconstruction steps
of a traditional processing chain, speeding up significantly the events
generation workflow. Through model optimization and hyperparameter tuning, we
achieve state-of-the-art precision on the jet four-momentum, while providing an
accurate description of the constituents momenta, and an inference time
comparable to that of a rule-based fast simulation.
