Measurement of Low-Activity Uranium Contamination using Bayesian Statistical Decision Theory

TL;DR

This paper introduces a Bayesian statistical decision framework for accurately detecting low-activity uranium contamination amidst fluctuating natural backgrounds, optimizing detection confidence and reducing false alarms.

Contribution

It presents an advanced Bayesian approach that adjusts confidence levels and balances detection accuracy, false alarms, and response time in nuclear contamination measurement.

Findings

Improves detection accuracy in fluctuating backgrounds

Balances true detection and false alarm rates effectively

Ensures timely responses in nuclear decommissioning scenarios

Abstract

Amongst the various technical challenges in the field of radiation detection is the need to carry out accurate low-level radioactivity measurements in the presence of large fluctuations in the natural radiation background, while lowering the false alarm rates. Several studies, using statistical inference, have been proposed to overcome this challenge. This work presents an advanced statistical approach for decision-making in the field of nuclear decommissioning. The results indicate that the proposed method allows to adjust the confidence degree in the stationarity of the background signal. It also ensures an acceptable tradeoff between the True Detection Rate (TDR), the False Alarm Rate (FAR) and the response time, and is consistent with the users requirements.