| Texto completo | |
| Autor(es): |
Touranakou, Mary
;
Chernyavskaya, Nadezda
;
Duarte, Javier
;
Gunopulos, Dimitrios
;
Kansal, Raghav
;
Orzari, Breno
;
Pierini, Maurizio
;
Tomei, Thiago
;
Vlimant, Jean-Roch
Número total de Autores: 9
|
| 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 |