LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows

Autor(es)

Orzari, Breno ; Chernyavskaya, Nadezda ; Cobe, Raphael ; Duarte, Javier ; Fialho, Jefferson ; Gunopulos, Dimitrios ; Kansal, Raghav ; Pierini, Maurizio ; Tomei, Thiago ; Touranakou, Mary

• Tipo de documento: Artigo Científico
• Fonte: MACHINE LEARNING-SCIENCE AND TECHNOLOGY; v. 4, n. 4, p. 12-pg., 2023-12-01.

Resumo

In high energy physics, one of the most important processes for collider data analysis is the comparison of collected and simulated data. Nowadays the state-of-the-art for data generation is in the form of Monte Carlo (MC) generators. However, because of the upcoming high-luminosity upgrade of the Large Hadron Collider (LHC), there will not be enough computational power or time to match the amount of needed simulated data using MC methods. An alternative approach under study is the usage of machine learning generative methods to fulfill that task. Since the most common final-state objects of high-energy proton collisions are hadronic jets, which are collections of particles collimated in a given region of space, this work aims to develop a convolutional variational autoencoder (ConVAE) for the generation of particle-based LHC hadronic jets. Given the ConVAE's limitations, a normalizing flow (NF) network is coupled to it in a two-step training process, which shows improvements on the results for the generated jets. The ConVAE+NF network is capable of generating a jet in 18.30 +/- 0.04 mu s , making it one of the fastest methods for this task up to now. (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: 19/16401-0 - Aprendizado de máquina para simulação de colisões de física de altas energias com o detector CMS
• Beneficiário: Breno Orzari
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado
• Processo FAPESP: 22/02950-5 - Desafios de computação para a fase II da atualização do CMS
• Beneficiário: Thiago Rafael Fernandez Perez Tomei
• Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial
• 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