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.
@article{arxiv.2301.10796,
title = {Reconstruction of Fast Neutron Direction in Segmented Organic Detectors using Deep Learning},
author = {Jun Woo Bae and Tingshiuan C. Wu and Igor Jovanovic},
journal= {arXiv preprint arXiv:2301.10796},
year = {2023}
}
Comments
15 pages. 9 figures. Preprint submitted to Elsevier August 2022