Autor(es)

Touranakou, Mary ; Chernyavskaya, Nadezda ; Duarte, Javier ; Gunopulos, Dimitrios ; Kansal, Raghav ; Orzari, Breno ; Pierini, Maurizio ; Tomei, Thiago ; Vlimant, Jean-Roch

• Tipo de documento: Artigo Científico
• Fonte: MACHINE LEARNING-SCIENCE AND TECHNOLOGY; v. 3, n. 3, p. 13-pg., 2022-09-01.

Resumo

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. (AU)

• Processo FAPESP: 20/06600-3 - Busca por matéria escura com partículas de vida longa no experimento CMS do LHC
• Beneficiário: Breno Orzari
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/25225-9 - Centro de Pesquisa e Análise de São Paulo
• Beneficiário: Sergio Ferraz Novaes
• Modalidade de apoio: Auxílio à Pesquisa - Projetos Especiais