RADIATION MONITORING | TETRIS & MACHINE LEARNING The key to this method of detection is proper
computerised reconstruction of the angles of arrival of the rays, based on the times each sensor detects the signal and the relative intensity each one detects, as reconstructed through an AI-guided study of simulated systems. Machine learning algorithms are implemented to analyse the detector reading, demonstrating great promise to reduce the need for detector pixel numbers and thereby reduce the cost of fabrication and deployment. Different configurations of four pixels were tried by the
researchers with an evaluation of the prediction accuracy of detectors which comprise with following four detector configurations: 2 × 2 square grid, and Tetrominos of S-, J-, T-shapes. An I-shaped Tetris detector array is not presented since it does not show performance good enough for directional mapping. Less symmetrical arrangements, the team found, provide
more useful information from a small array. While the S-shape detector worked with the smallest prediction followed by 2 × 2 square, J- and T-shapes. Although the T-shaped Tetris is the least accurate, all of the four types of detector could work enough to know the direction of the radiation source with about 1° of accuracy. Throughout the study, the authors assume that the incident beam energy is γ-radiation of 0.5 MeV, which they suggest is the realistic energy from pair production and comparable to many γ-decay energy levels.
Advancing detector AI Using neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction and shows this approach can potentially have a higher resolution than more complex alternatives. The authors suggest that experimental validation could further prove the capability of machine learning approach for locating radiation sources in the real-world scenario. In real-world applications of radiation mapping, the
authors note that it would be highly desirable to go beyond directional information and determine the exact position of a radiation source. They propose a method based on Maximum A Posteriori (MAP) estimation generate the distribution of radiation through the motion of the simple detector. The detector readout is simulated by MC given the detector’s initial position and orientation before
the detector begins to move in a circular motion. During the detector motion, the predicted source direction is calculated based on the model before the location of the radiation source is estimated via MAP based on the series of neural-network-inferred detector direction data at different detector positions. In an ideal case for one single radiation source, as few as two positions are enough to locate the source position while the circular motion and MAP are implemented for more complicated radiation profile mapping.
Using a moving detector with MAP thus took this development further and succeeded in localising the position of a radiation source. Field testing with a simple detector in a single-blind field test at the Berkeley Lab with a real caesium radiation source subsequently verified the capability of the MAP method for localisation of radiation sources with high accuracy in determining both the direction and distance to the source. This element of the research was led by Vavrek, in which the researchers at MIT did not know the location of the radiation source. In their paper, the authors note incidents like the
Fukushima Daiichi Nuclear Power Plant disaster in Japan in 2011 and the threat of a possible radiation release from Zaporizhzhia in the Ukrainian war zone underscoring the need for effective detection and monitoring of radioactive isotopes. The machine learning-based algorithm and aerial radiation detection will allow real-time monitoring and integrated emergency planning of radiological accidents for example. However, the authors also note that everyday operations
of nuclear reactors, uranium mining and processing, and the disposal of spent nuclear fuel also require monitoring of radioisotope release. In recent years, radiation localisation has also attracted increased interest with applications such as autonomous nuclear site inspection. In this study they authors focused on gamma-ray
sources but the computational tools they developed to extract directional information from the limited number of pixels aren’t restricted to particular wavelengths and can therefore also be used for neutrons, or even other forms of light, such as ultraviolet. The authors conclude that their framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection. ■
28 | July 2024 |
www.neimagazine.com
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