Modelling Radiation Sensor Angular Responses with Dynamic Linear Regression

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.