Bayesian non-parametric modeling by mixture of Kibble-Pólya tree to detect Low-Activity uranium contamination

•An innovative Bayesian algorithm based on a Pólya tree-type a priori is developed for detecting a low level of radioactivity under challenging conditions characterized by very low SNR and in the presence of a radiological background with significant variations in intensity and shape properties.•Bayesian test by mixture of Kibble-Pólya Tree (BKPT) test allows the consideration of correlations between counts recorded in adjacent channels, and thus the processing of output spectra from both high- and low-resolution detections.•BKPT test has demonstrated its effectiveness in terms of detection performance under very low SNR, proving advantageous against both stationary and non-stationary radiological backgrounds.•The superior performance of BKPT has been validated through comparisons with both Bayesian and frequentist hypothesis tests. Accurate detection of low-level radioactivity is critical for decommissioning projects in nuclear facilities, particularly in the design of radiation monitoring systems with a low false alarm rate. Utilizing a non-parametric Bayesian continuous probability distribution enables reliable mapping of potential contamination. Our method introduces a statistical test based on a Pólya tree prior, applied to radiation detection. The detection efficiency of this proposed Bayesian test is quantified using receiver-operating characteristic (ROC) curves and compared to a Bayesian test based on the Kibble bivariate gamma distribution developed for the same purpose. The results demonstrate that the new Bayesian test generally outperforms the previous method in terms of detection performance under very low signal-to-noise ratios, with improvements ranging from 3% to 28% against both stationary and non-stationary radiological backgrounds, respectively. This superiority is further reaffirmed through comparisons with alternative Bayesian and frequentist hypothesis tests, with gains estimated at 52% and 4%, respectively.