Particle-based fast jet simulation at the LHC with variational autoencoders. Issue 3 (1st September 2022)

• Record Type: Journal Article
• Authors: Touranakou, Mary; Chernyavskaya, Nadezda; Duarte, Javier; Gunopulos, Dimitrios; Kansal, Raghav; Orzari, Breno; Pierini, Maurizio; Tomei, Thiago; Vlimant, Jean-Roch
• Is Part Of: Machine learning: science and technology. Volume 3:Issue 3(2022)
• Publication Date: 2022-09-01
• Subjects: generative models -- sparse data simulation -- particle physics; 006.31
• DOI: 10.1088/2632-2153/ac7c56
• Languages: English
• ISSNs: 2632-2153

Abstract

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.