Combining CNN and Q-learning for increasing the accuracy of lost gamma source finding

Abstract The increasing use of nuclear technology in various fields makes it necessary to provide the required safety to work with this industry. Gamma source is one of the most widely used sources in industry and medicine. Finding a lost gamma source in a gamma irradiation room without human presence is challenging due to the particular arrangements and barriers in the room for radiation shielding and requires an efficient and robust method. In this paper, locating and routing the lost gamma source in the gamma irradiation room containing radiation blocking barriers are done simultaneously by using two methods, convolutional neural network (CNN) and Q-learning, which are powerful algorithms for deep learning and machine learning. Environment simulation with gamma source was performed using Geant4 simulation. The results show that by combining these two methods in geometries with radiation blocking barriers, in addition to locating with 90% accuracy, routing can also be performed. Although the presence of thick barriers in the room reduces the accuracy, increases the time required to finding the lost gamma source or the inefficiency of other methods, nevertheless, the results show that combination of CNN and Q-learning reduces the time and greatly increases the accuracy.

Abstract licence: CC BY-NC-ND

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