Modelling radiation sensor angular responses with dynamic linear regression

Tsitsimpelis, Ioannis and West, Andrew and Livens, Francis R. and Lennox, Barry and Taylor, C. James and Joyce, Malcolm J. (2024) Modelling radiation sensor angular responses with dynamic linear regression. In: 2024 UKACC 14th International Conference on Control (CONTROL) :. 2024 UKACC 14th International Conference on Control, CONTROL 2024 . IEEE, pp. 157-162. ISBN 9798350374278

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Abstract

Accurate characterization of radiation hotspots is a critical requirement for monitoring and decommissioning operations in the nuclear industry, particularly where the arrangement of contamination is complex, and the availability of ground-truth data is limited. This article develops a novel stochastic modelling approach that alleviates challenges often present in such operations. Initially, the experimentally derived angular responses of a collimated single detector apparatus at different energy regions (counts over radiation footprints) are expressed by two functions: the Fourier transform of a rectangular pulse (approximated by a sinc function) and a Moffat function. Subsequently, these are both framed within a Dynamic Linear Regression (DLR) model. The resulting Moffat/sinc-DLR models enhance the quality of the fit to experimental data, and improve the accuracy and resolution of radiation localization, thus showcasing the value of such methods for radiation characterization tasks.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
Research Output Funding/yes_externally_funded
Subjects:
?? radiation detectorsource localizationsinc functionmoffat functiondynamic linear regression (dlr)yes - externally funded ??
ID Code:
218705
Deposited By:
Deposited On:
11 Jun 2024 11:20
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Jun 2024 12:06