| Autor: |
Hanan Arahmane, Jonathan Dumazert, Eric Barat, Thomas Dautremer, Frédérick Carrel, Nicolas Dufour, Maugan Michel |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Results in Physics, Vol 64, Iss , Pp 107874- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2211-3797 |
| DOI: |
10.1016/j.rinp.2024.107874 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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