2020
DOI: 10.1038/s41598-020-62947-3
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Expectation-propagation for weak radionuclide identification at radiation portal monitors

Abstract: 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 na… Show more

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Cited by 7 publications
(11 citation statements)
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References 28 publications
(34 reference statements)
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“…In this application, since the photon detector can only capture a single gamma photon event within each time step and all the neuron computations are processed in TDM manner, each membrane voltage update can be simply implemented by adding the weight of connection with the pre-synaptic neuron that fired, this can be described by equation (2). The reset by subtraction approach is applied when the membrane voltage is above the threshold, which is shown in equation (3). Compared with the reset by zero manner, it is more suitable for ANN-to-SNN conversion, which is reported by [6] and also proved by our experiments.…”
Section: B Pooling Layer and Fully-connected Layermentioning
confidence: 99%
See 1 more Smart Citation
“…In this application, since the photon detector can only capture a single gamma photon event within each time step and all the neuron computations are processed in TDM manner, each membrane voltage update can be simply implemented by adding the weight of connection with the pre-synaptic neuron that fired, this can be described by equation (2). The reset by subtraction approach is applied when the membrane voltage is above the threshold, which is shown in equation (3). Compared with the reset by zero manner, it is more suitable for ANN-to-SNN conversion, which is reported by [6] and also proved by our experiments.…”
Section: B Pooling Layer and Fully-connected Layermentioning
confidence: 99%
“…Each target radioisotope has a characteristic energy histogram meaning that radioisotope ID can be carried out as a histogram classification task. Many algorithms have been studied and applied to this task [1][2] [3].…”
Section: Introductionmentioning
confidence: 99%
“…Bayesian approaches in radiation data analytics in a machine learning framework have been proposed in various forms. In [32], an identification algorithm has been proposed utilizing sequential Bayesian Learning, while in [33] a simple Bayesian regression algorithm for the identification of weak signatures is presented and tested. Furthermore, a simple Bayesian inference approach, which was tested on a set of 6 nuclides, was introduced in [34].…”
Section: Spectrum Analysismentioning
confidence: 99%
“…The prompt detection of radioactive sources must be performed in a short time window of a few seconds to keep traffic flowing properly.The performance of a portal monitor in terms of sensitivity, i.e., maximization of the positive detection rate, depends on the detection efficiency of the system and its form factor, which should be optimized for a specific application [3]. In previous studies [4], we experimentally demonstrated the use of a sparsity-promoting Bayesian algorithm capable of unmixing the signatures from weak gamma-ray sources, detected by organic scintillators. Our algorithm, hereafter referred to as the unmixing algorithm, allowed to identify radioactive sources based on measured spectra consisting of less than 500 counts despite the relatively low energy resolution featured by organic scintillators.…”
Section: Background and Motivationmentioning
confidence: 99%
“…Our algorithm, hereafter referred to as the unmixing algorithm, allowed to identify radioactive sources based on measured spectra consisting of less than 500 counts despite the relatively low energy resolution featured by organic scintillators. In an unknown spectrum with approximately 1000 counts, the algorithm is able to identify up to three gamma emitting radionuclides, and a few hundred of counts from weapons grade plutonium results in an alarm rate of 80% [4]. The algorithm relies on a pre-compiled library of radionuclides to correctly identify the mixture components.…”
Section: Background and Motivationmentioning
confidence: 99%