Development of a Bayesian linear regression model for the detection of a weak radiological source from gamma spectra measurements
Date
2021
Authors
Meengs, Matthew, author
Brandl, Alexander, advisor
Johnson, Thomas E., committee member
Sudowe, Ralf, committee member
Kokoszka, Piotr, committee member
Journal Title
Journal ISSN
Volume Title
Abstract
The detection of radiation requires the use of statistical tools due to the probabilistic nature of the emission and the interaction properties of radiation. Frequentist statistical methods are traditionally employed towards this end – the most common being the "traditional" method which calculates a decision threshold above which a source is determined to be present. The decision threshold is calculated from a predetermined false positive rate (typically 5%) and is used as a decision parameter. The decision parameter is a statistical tool by which it is determined whether or not a source other than background is present. In radiological conditions where a source is both improbable and weak, and where counting time is limited, the detection of a source becomes progressively more challenging using this traditional method. The detection of clandestine fissile materials presents such a challenge, and with the increasing risk of nuclear proliferation, there exists a growing motivation to research more optimal methods of detection, especially where a missed detection is of such high consequence. Previous research has been conducted on using a Bayesian model to develop a decision parameter for weak source detection. The use of a Bayesian model has been shown in laboratory settings to outperform the traditional frequentist method. However, the model tested was designed for gross counts only. In the present study, a Bayesian algorithm is being developed and tested that uses the entirety of the gamma spectrum. Specifically, several Bayesian linear regressions are developed and tested which compared different energy ranges in the spectrum. The parameters generated from these linear regressions are tested for their efficacy as decision parameters. With the additional information presented from the entire spectrum, it is theoretically possible that even further improvements in the detection of a weak source can be achieved. The results of this research have shown that regressor coefficients via a Bayesian method are effective as decision parameters. The best results, however, were shown only to match the efficacy of the more traditional, frequentist method of detection.