Particle-based fast jet simulation at the LHC with variational autoencoders

• Autor(a) principal: Touranakou, Mary
• Data de Publicação: 2022
• Outros Autores: Chernyavskaya, Nadezda, Duarte, Javier, Gunopulos, Dimitrios, Kansal, Raghav, Orzari, Breno [UNESP], Pierini, Maurizio, Tomei, Thiago [UNESP], Vlimant, Jean-Roch
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1088/2632-2153/ac7c56; http://hdl.handle.net/11449/240570
• topic: generative models; particle physics; sparse data simulation

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.