Abstract
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.
Highlights
Radio-Isotope IDentification (RIID) algorithms have broad application across alarms and detection, identification, and mapping [1]. Often these are developed in the context of security, but are readily applied to problems in decommissioning and survey [2,3,4,5,6]
The goal is to infer the identity of all isotopes present in a source and to quantify as many properties as possible [6]
There are a myriad of competing effects that can impact gamma ray spectra such as shielding, gain shifts, and changing environmental conditions
Summary
Generalised gamma spectrometry simulator for problems in nuclide identification Turner, Anthony; Wheldon, Carl; Gilbert, Mark; Packer, Lee; Burns, Jon; Kokalova Wheldon, Tzany; Freer, Martin. Document Version Publisher's PDF, known as Version of record Citation for published version (Harvard): Turner, A, Wheldon, C, Gilbert, M, Packer, L, Burns, J, Kokalova Wheldon, T & Freer, M 2020, 'Generalised gamma spectrometry simulator for problems in nuclide identification', Journal of Physics: Conference Series, vol 1643, 012211.
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