Expectation-propagation for weak radionuclide identification at radiation portal monitors

Yoann Altmann, Angela Di Fulvio, Marc G. Paff, Shaun D. Clarke, Mike E. Davies, Stephen McLaughlin, Alfred O. Hero, Sara A. Pozzi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
52 Downloads (Pure)


We propose a sparsity-promoting Bayesian algorithm capable of identifying radionuclide signatures from weak sources in the presence of a high radiation background. The proposed method is relevant to radiation identification for security applications. In such scenarios, the background typically consists of terrestrial, cosmic, and cosmogenic radiation that may cause false positive responses. We evaluate the new Bayesian approach using gamma-ray data and are able to identify weapons-grade plutonium, masked by naturally-occurring radioactive material (NORM), in a measurement time of a few seconds. We demonstrate this identification capability using organic scintillators (stilbene crystals and EJ-309 liquid scintillators), which do not provide direct, high-resolution, source spectroscopic information. Compared to the EJ-309 detector, the stilbene-based detector exhibits a lower identification error, on average, owing to its better energy resolution. Organic scintillators are used within radiation portal monitors to detect gamma rays emitted from conveyances crossing ports of entry. The described method is therefore applicable to radiation portal monitors deployed in the field and could improve their threat discrimination capability by minimizing "nuisance" alarms produced either by NORM-bearing materials found in shipped cargoes, such as ceramics and fertilizers, or radionuclides in recently treated nuclear medicine patients.

Original languageEnglish
Article number6811
JournalScientific Reports
Publication statusPublished - 22 Apr 2020

ASJC Scopus subject areas

  • General


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