Rapid imaging of special nuclear materials for nuclear nonproliferation and terrorism prevention

Abstract

We introduce a neutron-gamma emission tomography (NGET) technique for rapid detection, three-dimensional imaging, and characterization of special nuclear materials like weapons-grade plutonium and uranium. The technique is adapted from fundamental nuclear physics research and represents a previously unexplored approach to the detection and imaging of small quantities of these materials. The method is demonstrated on a radiation portal monitor prototype system based on fast organic scintillators, measuring the characteristic fast time and energy correlations between particles emitted in nuclear fission processes. The use of these correlations in real time in conjunction with modern machine learning techniques provides unprecedented imaging efficiency and high spatial resolution. This imaging modality addresses global security threats from terrorism and the proliferation of nuclear weapons. It also provides enhanced capabilities for addressing different nuclear accident scenarios and for environmental radiological surveying.

A novel 3D-imaging and characterisation technique for special nuclear materials in radioactive waste

“…ARCTERIX is based on a novel 3D radiation imaging modality for SNM -neutron-gamma emission tomography (NGET) [6,7], which was inspired by methods developed in fundamental nuclear physics experiments [8]. The method has similarities with emission tomographic techniques used in medical imaging, such as positron emission tomography (PET) [9,10] and its variant time-of-flight PET (TOFPET) [11], which also use the physics of the emission process as a means to locate radioactive sources with high precision.…”

“…The simulation codes are used to optimise the detection geometry for different types of radioactive waste configurations. The novelty of the ARCTERIX approach to visualising radiation from nuclear materials lies in the combination of rapid and sensitive detection of the radiation that they emit by using measured detailed time and energy correlations between detected particles [6]. The rapid, precise and automatic determination of the location of SNM in the waste matrix leads to key advantages in radioactive waste characterisation.…”

“…A ML algorithm based on a four-layer feed-forward ANN was used to process the data. See reference [6] for details.…”

“…The digitiser has pulse shape discrimination (PSD) capabilities for distinguishing between neutron and gamma-ray interactions in the scintillators based on field programmable gate arrays (FPGA). For further details concerning the detection assembly and readout electronics, see Reference [6]. The digitised signals ("traces") from the detector modules are processed in real time within the digitiser's FPGAs to extract charge integrals, pulse shape information and timing information.…”

Laser-induced fluorescence experimental spectroscopy and theoretical calculations of uranium monoxide

“…Nuclear power can generate large amounts of energy with low emissions to the atmosphere, making it an attractive alternative to fossil fuels. [1][2][3][4] However, it also has some drawbacks, such as long-term storage of nuclear waste and amelioration of existing waste. Uranium monoxide (UO) is an important component in nuclear energy, so the study of UO has contributed to exploring not only the solution of nuclear waste but bonding characteristics and electronic structure of actinides.…”