Generalised gamma spectrometry simulator for problems in nuclide identification

Improvements in Radio-Isotope IDentification (RIID) algorithms have always been a continuous research focus. However, significant developments in machine learning have recently sparked renewed interest. To provide a rapid development environment for this, a generalised gamma simulator has been built...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-12, Vol.1643 (1), p.12211
Main Authors: Turner, A., Wheldon, C., Gilbert, M.R., Packer, L.W., Burns, J., Kokalova Wheldon, Tz, Freer, M.
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Machine learning
Neural networks
Physics
Radiation shielding
Radiation sources
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Summary:Improvements in Radio-Isotope IDentification (RIID) algorithms have always been a continuous research focus. However, significant developments in machine learning have recently sparked renewed interest. To provide a rapid development environment for this, a generalised gamma simulator has been built using the GEANT4 toolkit. This enables consideration of a diverse range of radiation sources and shielding scenarios. The simulator currently provides training data for the development of neural network based RIID models.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1643/1/012211