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arXiv · 2301.10796

Reconstruction of Fast Neutron Direction in Segmented Organic Detectors using Deep Learning

Jun Woo Bae·Tingshiuan C. Wu·Igor Jovanovic
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Abstract

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. 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.

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Jun Woo Bae, Tingshiuan C. Wu, Igor Jovanovic. 2023-01-25. Reconstruction of Fast Neutron Direction in Segmented Organic Detectors using Deep Learning. https://doi.org/10.1016/j.nima.2023.168024

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