Abstract

Active interrogation (AI) is a promising technique to detect shielded special nuclear materials (SNMs). At the University of Michigan, we are developing a photon-based AI system that uses bremsstrahlung radiation from an electron linear accelerator (linac) as an ionizing source and trans-stilbene organic scintillating detectors for neutron detection. Stilbene scintillators are sensitive to fast neutrons and photons and have excellent pulse shape discrimination (PSD) capabilities. The traditional charge integration (CI) method commonly used for PSD analysis eliminates piled-up pulses and relies on a particle discrimination line to separate neutrons and photons. The presence of the intense photon flux during AI creates a significant number of piled-up events in the stilbene scintillator, thereby posing a great challenge to the traditional CI method. Identifying true single neutron pulses becomes challenging due to the presence of a pile-up cloud and overlapping neutron, photon and pile-up clouds in the PSD analysis. To mitigate the effect of pulse pile-up and identify true single neutron pulses from stilbene scintillators, an artificial neural network (ANN) system is developed. The developed ANN system identifies single neutron pulses and neutron-photon combinations from piled-up events. The results obtained from a 252Cf measurement in the presence of the intense photon flux show that the developed ANN system outperforms the traditional CI method. Since many piled-up events lie above the particle discrimination line, they get misclassified as neutrons by the traditional CI method resulting in 27% overestimation of the net neutron count rate during the linac pulse. The overall net neutron count rate (single and restored neutrons) during the linac pulse, estimated by the ANN system is 62.32% of the ground truth. Energy spectroscopy of the ANN attributed single neutron pulses further provides evidence on the detection of prompt fission neutrons from the 252Cf fission source.

Highlights

  • Non-destructive assay (NDA) techniques, classified as either passive or active, are widely used to detect radiation emitted from special nuclear materials (SNMs)

  • The developed photon-based Active interrogation (AI) system aims at detecting neutrons during the linac pulse because prompt fission neutrons are more abundant and energetic than delayed neutrons that could be detected between linac pulses

  • We attempted to provide a solution to SNM detection using active interrogation with commercially available economical equipment

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Summary

INTRODUCTION

Non-destructive assay (NDA) techniques, classified as either passive or active, are widely used to detect radiation emitted from special nuclear materials (SNMs). An artificial neural network (ANN) system has been developed to mitigate the effect of pulse pile-up in transstilbene organic scintillators during photon AI of SNM. Our ANN system, in combination with two cleansers, presents a novel approach for classifying single and piled-up pulses from a trans-stilbene organic scintillator. If the ratio exceeds a set threshold value, the pulse is flagged as a misclassified single pulse Both the encoder and decoder use a log sigmoid activation function. The threshold is user-specified and all piled-up events that pass the voltage threshold check on the second pulse are sent to the Classify Pile-Up NN for further classification

TRAINING OF THE DEVELOPED ANN SYSTEM
RESULTS
CONCLUSION
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