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.
@article{arxiv.2203.00520,
title = {Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders},
author = {Mary Touranakou and Nadezda Chernyavskaya and Javier Duarte and Dimitrios Gunopulos and Raghav Kansal and Breno Orzari and Maurizio Pierini and Thiago Tomei and Jean-Roch Vlimant},
journal= {arXiv preprint arXiv:2203.00520},
year = {2022}
}