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

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...

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Bibliographic Details
Published in:arXiv.org 2023-01
Main Authors: Jun Woo Bae, Wu, Tingshiuan C, Jovanovic, Igor
Format: Article
Language:English
Subjects:
Algorithms
Angular resolution
Deep learning
Detectors
Fast neutrons
Machine learning
Neutron scattering
Neutrons
Recurrent neural networks
Scattering
Scintillation counters
Sensors
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2301.10796