Particle-based fast jet simulation at the LHC with variational autoencoders. Issue 3 (1st September 2022) - Touranakou, Mary; Chernyavskaya, Nadezda; Duarte, Javier; Gunopulos, Dimitrios; Kansal, Raghav; Orzari, Breno; Pierini, Maurizio; Tomei, Thiago; Vlimant, Jean-Roch
Abstract: We study how to use deep variational autoencoders (VAEs) for a fast simulation of jets of particles at the Large Hadron Collider. 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 VAE 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.