Design and Calibration of an Optically Segmented Single Volume Scatter Camera for Neutron Imaging

Abstract

The Optically Segmented Single Volume Scatter Camera (OS-SVSC) aims to image neutron sources for non-proliferation applications using the kinematic reconstruction of elastic double-scatter events. Our prototype system consists of 64 EJ-204 organic plastic scintillator bars, each measuring 5 mm $\times$ 5 mm $\times$ 200 mm and individually wrapped in Teflon tape. The scintillator array is optically coupled to two silicon photomultiplier ArrayJ-60035 64P-PCB arrays, each comprised of 64 individual 6 $\times$ 6 mm J-Series sensors arranged in an 8 $\times$ 8 array. We detail our calibration efforts, beginning with calibrations for the electronics, based on the IRS3D application-specific integrated circuits, and their associated timing resolutions, ranging from 30 ps to 90 ps. With electronics calibrations applied, energy and position calibrations were performed for a set of edge bars using ${}^{22}$Na and ${}^{90}$Sr, respectively, reporting an average resolution of (12.07$\pm$0.03) mm for energy depositions between 900 keVee and 1000 keVee. We further demonstrate a position calibration method for the internal bars of the matrix using cosmic-ray muons as an alternative to emission sources that cannot easily access these bars, with an average measured resolution of (14.86$\pm$0.29) mm for depositions between 900 keVee and 1000 keVee. The coincident time resolution reported between pairs of bars measured up to 400 ps from muon acquisitions. Energy and position calibration values measured with muons are consistent with those obtained using particle emission sources.

arXiv comment24 pages, 32 figures. Manuscript submitted to Jinst

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Cited by in corpus (4)

• Design and Characterization of an Optically-Segmented Single Volume Scatter Camera Module
• Reconstruction of Fast Neutron Direction in Segmented Organic Detectors using Deep Learning
• Evaluation of a Positron-Emission-Tomography-based SiPM readout for Compact Segmented Neutron Imagers
• Investigating Resource-efficient Neutron/Gamma Classification ML Models Targeting eFPGAs