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Dataset for neutron and gamma-ray pulse shape discrimination: radiation pulse signals and discrimination methodologies

Li, Peng ; Liu, Haoran

Zenodo 2024

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  • Título:
    Dataset for neutron and gamma-ray pulse shape discrimination: radiation pulse signals and discrimination methodologies
  • Autor: Li, Peng ; Liu, Haoran
  • Assuntos: Charge comparison ; Heterogeneous quasi-continuous spiking cortical model ; Ladder gradient ; Neutron and gamma-ray discrimination ; Pulse shape discrimination ; Pulse-coupled neural network ; Zero crossing
  • Notas: 10.5281/zenodo.7754573
    10.1007/s41365-022-01054-6
    RelationTypeNote: IsSupplementedBy -- 10.1007/s41365-022-01054-6
    RelationTypeNote: IsSupplementedBy -- 10.1007/s41365-022-01136-5
    RelationTypeNote: HasVersion -- 10.5281/zenodo.7754573
    10.1007/s41365-022-01136-5
    10.1007/s41365-021-00915-w
    arXiv:2305.18242
    RelationTypeNote: IsSupplementedBy -- 10.1007/s41365-021-00915-w
    RelationTypeNote: HasVersion -- 10.5281/zenodo.10947029
    MATLAB R2022B is the recommended test environment for this dataset. Detailed descriptions of this dataset can be found at: https://doi.org/10.48550/arXiv.2305.18242.
    10.5281/zenodo.10947029
  • Descrição: This dataset provides neutron and gamma-ray pulse signals for pulse shape discrimination experiments. Serval traditional and recently proposed pulse shape discrimination algorithms are utilized to conduct pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms include zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and heterogeneous quasi-continuous spiking cortical model (HQC-SCM). This dataset also provides the source code of all these pulse shape discrimination methods, together with the source code of schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. Detailed descriptions of this dataset can be found at: https://doi.org/10.48550/arXiv.2305.18242.
  • Editor: Zenodo
  • Data de criação/publicação: 2024
  • Idioma: Inglês

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