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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...
| Autores principales: | , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2784343 |