Particle Cloud Generation with Message Passing Generative Adversarial Networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversity of California San Diego-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEuropean Organization for Nuclear Research (CERN)-
Autor(es): dc.contributorCalifornia Institute of Technology-
Autor(es): dc.contributorUniversity of Athens-
Autor(es): dc.creatorKansal, Raghav-
Autor(es): dc.creatorDuarte, Javier-
Autor(es): dc.creatorSu, Hao-
Autor(es): dc.creatorOrzari, Breno-
Autor(es): dc.creatorTomei, Thiago-
Autor(es): dc.creatorPierini, Maurizio-
Autor(es): dc.creatorTouranakou, Mary-
Autor(es): dc.creatorVlimant, Jean-Roch-
Autor(es): dc.creatorGunopulos, Dimitrios-
Data de aceite: dc.date.accessioned2025-08-21T19:06:09Z-
Data de disponibilização: dc.date.available2025-08-21T19:06:09Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240249-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240249-
Descrição: dc.descriptionIn 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 Fréchet 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.-
Descrição: dc.descriptionUniversity of California San Diego, La Jolla-
Descrição: dc.descriptionUniversidade Estadual Paulista, SP-
Descrição: dc.descriptionEuropean Organization for Nuclear Research (CERN)-
Descrição: dc.descriptionCalifornia Institute of Technology-
Descrição: dc.descriptionNational and Kapodistrian University of Athens-
Descrição: dc.descriptionUniversidade Estadual Paulista, SP-
Formato: dc.format23858-23871-
Idioma: dc.languageen-
Relação: dc.relationAdvances in Neural Information Processing Systems-
???dc.source???: dc.sourceScopus-
Título: dc.titleParticle Cloud Generation with Message Passing Generative Adversarial Networks-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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