Generalised gamma spectrometry simulator for problems in nuclide identification

Anthony Turner; Carl Wheldon; Mark Gilbert; Lee Packer; Jon Burns; Tzany Kokalova Wheldon; Martin Freer

doi:10.1088/1742-6596/1643/1/012211

Generalised gamma spectrometry simulator for problems in nuclide identification

Anthony Turner
, Carl Wheldon
, Mark Gilbert
, Lee Packer
, Jon Burns
, Tzany Kokalova Wheldon
, Martin Freer

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	012211
Journal	Journal of Physics: Conference Series
Volume	1643
DOIs	https://doi.org/10.1088/1742-6596/1643/1/012211
Publication status	Published - 23 Dec 2020
Event	27th International Nuclear Physics Conference (INPC2019) - Glasgow, United Kingdom Duration: 29 Jul 2019 → 2 Aug 2019 Conference number: 27th

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TurnerA2021GeneralisedFinal published version, 1.54 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Generalised gamma spectrometry simulator for problems in nuclide identification",

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.",

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AU - Wheldon, Carl

AU - Gilbert, Mark

AU - Packer, Lee

AU - Burns, Jon

AU - Kokalova Wheldon, Tzany

AU - Freer, Martin

N1 - Conference code: 27th

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N2 - 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.

AB - 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.

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DO - 10.1088/1742-6596/1643/1/012211

M3 - Article

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JO - Journal of Physics: Conference Series

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T2 - 27th International Nuclear Physics Conference (INPC2019)

Y2 - 29 July 2019 through 2 August 2019

ER -

Generalised gamma spectrometry simulator for problems in nuclide identification

Abstract

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