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

TL;DR

This paper presents a deep learning approach using recurrent neural networks to accurately reconstruct the direction of fast neutron sources with fewer detection events compared to traditional methods, enhancing nuclear material detection capabilities.

Contribution

It introduces a novel deep learning model for neutron source direction reconstruction that outperforms conventional algorithms in efficiency and accuracy.

Findings

Achieves 0.301 rad uncertainty with 100 events

Requires 75% fewer events than traditional methods for similar accuracy

Potentially improves neutron detection in nuclear security applications

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.