Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC

We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of hadronic jets due to its sparsity and the possibility of evalua...

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Detalles Bibliográficos
Autores principales: Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Duarte, Javier, Kansal, Raghav, Vlimant, Jean-Roch, Gunopulos, Dimitrios
Lenguaje:eng
Publicado: 2021
Materias:
hep-ex
Particle Physics - Experiment
physics.data-an
Other Fields of Physics
Acceso en línea:http://cds.cern.ch/record/2784343

Internet

http://cds.cern.ch/record/2784343

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