Special Nuclear Material Detection Using Trans-Stilbene and Artificial Neural Network

Active interrogation (AI) is a promising approach to detect shielded special nuclear materials (SNMs). During AI, SNM targets are bombarded with an ionizing radiation resulting in nuclear fission reactions that produces prompt fission neutrons up to 10 MeV in energy (Watt energy spectrum). At the Un...

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Bibliographic Details
Published in2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) pp. 1 - 3
Main Authors Jinia, Abbas J., Maurer, Tessa E., Clarke, Shaun D., Kim, Hun-Seok, Wentzloff, David D., Pozzi, Sara A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.10.2021
Subjects
artificial neural network
Artificial neural networks
Bremsstrahlung
Lead
linac
Linear particle accelerator
Neutrons
prompt fission neutrons
pulsed bremsstrahlung radiation
Scintillators
stilbene
Uranium
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Summary:Active interrogation (AI) is a promising approach to detect shielded special nuclear materials (SNMs). During AI, SNM targets are bombarded with an ionizing radiation resulting in nuclear fission reactions that produces prompt fission neutrons up to 10 MeV in energy (Watt energy spectrum). 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 scintillation detectors for prompt neutron detection. The stilbene scintillator is sensitive to fast neutrons and photons and has excellent pulse shape discrimination (PSD) capabilities. The pulsed bremsstrahlung linac radiation used in AI creates significant piled-up pulses in the stilbene scintillator posing a great challenge to traditional methods used for PSD. To address pulse pile-up issues during AI measurements, we developed an artificial neural network system for digital pulse processing of scintillation pulses. We present results from the bombardment of bremsstrahlung radiation on a depleted uranium target. The first results obtained using the trained neural network system indicates the presence of prompt fission neutrons produced as a result of (g, f) reactions in the depleted uranium. The distribution of energy deposited by the photoneutrons provide further evidence on the detection of prompt fission neutrons.
ISSN:2577-0829
DOI:10.1109/NSS/MIC44867.2021.9875575