| Texto completo | |
| Autor(es): Mostrar menos - |
Kansal, Raghav
;
Duarte, Javier
;
Su, Hao
;
Orzari, Breno
;
Tomei, Thiago
;
Pierini, Maurizio
;
Touranakou, Mary
;
Vlimant, Jean-Roch
;
Ranzato, M
;
Beygelzimer, A
;
Dauphin, Y
;
Liang, PS
;
Vaughan, JW
Número total de Autores: 13
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017); v. 34, p. 14-pg., 2021-01-01. |
| Resumo | |
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generative adversarial networks (GANs), have the potential to significantly accelerate LHC jet simulations. However, despite jets having a natural representation as a set of particles in momentum-space, a.k.a. a particle cloud, there exist no generative models applied to such a dataset. In this work, we introduce a new particle cloud dataset (JetNet), and apply to it existing point cloud GANs. Results are evaluated using (1) 1-Wasserstein distances between high- and low-level feature distributions, (2) a newly developed Frechet ParticleNet Distance, and (3) the coverage and (4) minimum matching distance metrics. Existing GANs are found to be inadequate for physics applications, hence we develop a new message passing GAN (MPGAN), which outperforms existing point cloud GANs on virtually every metric and shows promise for use in HEP. We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models. Additionally, to facilitate research and improve accessibility and reproducibility in this area, we release the open-source JETNET Python package with interfaces for particle cloud datasets, implementations for evaluation and loss metrics, and more tools for ML in HEP development. (AU) | |
| 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: | 18/25225-9 - Centro de Pesquisa e Análise de São Paulo |
| Beneficiário: | Sergio Ferraz Novaes |
| Modalidade de apoio: | Auxílio à Pesquisa - Projetos Especiais |
| Processo FAPESP: | 18/01398-1 - Busca por nova física no experimento CMS do Large Hadron Collider |
| Beneficiário: | Breno Orzari |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |