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Development of a radionuclide identifier based on nuclear spectroscopy and machine learning techniques

Abstract

In radiological emergencies, decommissioning of nuclear facilities and monitoring of cargo, borders and potentially contaminated sites, the use of a system for real-time identification of radionuclides present in a given location or sample is necessary to ensure nuclear safety and prevent irregular transport of radioactive materials. Also of interest is the monitoring of radionuclides used for diagnosis and treatment of diseases in nuclear medicine services, of radiotracers used in fluid flow measurement in turbines and heat exchangers, effluent treatment, and evaluation of industrial mixers. In addition, in the exploration and exploitation of mineral resources, gamma and neutron sources need to be monitored when used in the determination of density, porosity, moisture, and hydrocarbon content. For all these scenarios, the International Atomic Energy Agency recommends a priority list of radionuclides to be considered in these situations. The process of radionuclide identification is based on nuclear spectroscopy techniques, using detectors that record the charge or height of electrical pulses generated by radiation from the decay of radionuclides. The registration of these events allows the construction of a distribution of charges as a function of their frequency of occurrence, which is converted into an energy spectrum for the study of the properties of the incident radiation. Thus, spectral analysis allows the identification of radionuclides and the determination of relevant parameters such as activity and emission probability of the radionuclide. The process of radionuclide identification is based on nuclear spectroscopy techniques, using detectors that record the charge or height of electrical pulses generated by radiation from the decay of radionuclides. The registration of these events allows the construction of a distribution of charges as a function of their frequency of occurrence, which is converted into an energy spectrum for the study of the properties of the incident radiation. Thus, spectral analysis allows the identification of radionuclides and the determination of relevant parameters such as activity and emission probability of the radionuclide. Due to the extensive and intensive need for radionuclide monitoring, it is of interest to balance costs with radionuclide identifying devices. In low-cost detectors that have limited energy resolution and low detection efficiency, or when real-time results are desired with minimal exposure to the radioactive source, the radionuclide identification process becomes a challenge. In such cases, where there are overlapping peaks in the spectrum or insufficient statistical event counts, algorithms tend to fail and working with complex samples becomes unfeasible. Based on results obtained in PIPE phase 1, the use of machine learning methods can enable devices based on these technologies without affecting the reliability of the final results, allowing better decision-making. Thus, in this project we propose the construction of a radionuclide identifier device with digital pulse processing based on more economical detectors with lower energy resolution, such as monolithic scintillator crystals coupled to silicon photomultipliers. In this work, the performance of different detectors based on scintillator crystals and silicon photomultipliers will be evaluated with respect to cost and accuracy of radionuclide identification. The signals from the detectors will be digitized and processed in an electronic system for power spectrum generation, quantification and radionuclide identification. The machine learning algorithms for multiple radionuclide identification, developed in phase 1 of the PIPE program, will be applied in an embedded system and the validation and detection tests will be performed in the Applied Nuclear Physics Laboratory of the Institute for Energy and Nuclear Research. (AU)

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