Reconstruction of fast neutron direction in segmented organic detectors using deep learning

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 2023-01, Vol.1049
• Main Authors: Bae, Jun Woo, Wu, Tingshiuan C., Jovanovic, Igor
• Format: Article
• Language: English
• Subjects: Deep learning; Fast neutron detection; INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Neutron direction; Organic detector; Recurrent neural network
• ISSN: 0168-9002; 1872-9576

A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. Here, the model is based on recurrent neural network, which can be trained by a sequence of data obtained from an event recorded in the detector and suitably pre-processed. The performance of deep learning-based model is compared with the conventional double-scatter detection algorithm in reconstructing the direction of a fast neutron source. With the deep learning model, the uncertainty in source direction of 0.301 rad is achieved with 100 neutron detection events in a segmented cubic organic scintillator detector with a side length of 46 mm. To reconstruct the source direction with the same angular resolution as the double-scatter algorithm, the deep learning method requires 75% fewer events. Application of this method could augment the operation of segmented detectors operated in the neutron scatter camera configuration for applications such as special nuclear material detection.