Title: An Artificial Neural Network System for Photon-Based Active Interrogation Applications

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 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 photonmore » 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 25% 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 60% 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.« less

Authors:

Jinia, Abbas J. ^[1]; Maurer, Tessa E. ^[1]; Meert, Christopher A. ^[1]; Hua, Michael Y. ^[1]; Clarke, S. D. ^[1]; Kim, Hun-Seok ^[1]; Wentzloff, David D. ^[1]; Pozzi, Sara A. ^[1]

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+ Show Author Affiliations

1. Univ. of Michigan, Ann Arbor, MI (United States)

Publication Date:

Fri Aug 27 00:00:00 EDT 2021

Research Org.:

Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1826368

Grant/Contract Number:  

NA0003920

Resource Type:

Accepted Manuscript

Journal Name:

IEEE Access

Additional Journal Information:

Journal Volume: 9; Journal ID: ISSN 2169-3536

Publisher:

IEEE

Country of Publication:

United States

Language:

English

Subject:

42 ENGINEERING; Artificial neural network; high photon flux; linac; pile-up recovery; trans-stilbene