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Recent Research

Optimized Detection of Radioisotopes

Detection and identification of weak radiation sources, in the presence of backgrounds, requires sensitive detectors and optimized detection algorithms.  For PVT detectors with a small number of energy bins, we developed a metric that efficiently measures the directional separation between measurements containing a radioisotope of interest and background measurements.  Optimized detection is achieved by a proper combination of energy bins that depend on the radioisotope.  An independent metric is evaluated for each radioisotope or source of interest.  The detection thresholds for the metrics are determined by constrained nonlinear optimization that maximizes the probability of detection (PD) while holding the probability of false alarms (PFA) constant.  PD and PFA are evaluated as functions of the metric thresholds by direct use of large datasets containing the radioisotopes of interest (for evaluation of PD) and backgrounds or benign sources (for evaluation of PFA).

Near-surface Detection in Maritime Environments

We invented a new maritime surveillance and detection concept based on Raman scattering of laser light off water molecules.  This concept avoids the often problematic elastic scattering from the surface, from impurities in the water, or from the bottom surface for shallow waters.  Using a range-gated or time-resolved scanning lidar that detects Raman scattered photons from water, the absence or change of signal indicates the presence of a non-water object.  With sufficient spatial resolution, a two dimensional outline of the object can be generated by the scanning lidar.  The maximum detection depth for this concept is limited by the attenuation of the excitation and return Raman light in water.  We also developed a lidar model for this concept and carried out proof-of-concept measurements.  Using published values of the Raman cross section, the model and measurements are in good agreement and show that a sufficient number of Raman photons can be generated for modest lidar parameters to make this concept useful for near-surface detection.   https://www.osapublishing.org/ao/abstract.cfm?uri=ao-57-17-4858

Circuit Modeling for Conductivity Imaging

Detection of anomalous shielding materials is a possible indicator of the presence of nuclear or radiological materials.  Conductivity imaging (mapping of electrical conductivity profiles in two or three dimensions) is a potential method for detection of such scenarios when the radiation signal is not detectable.  We developed a simple circuit model (both frequency and time domains) for scoping and scaling studies of conductivity imaging using pulsed coils.  This model is used to calculate magnetic fields as functions of frequency or time at sensor locations to study effects of conductors, including conductors within other conductors, on the detected fields.  Currently the model uses axisymmetric elements.  Research is continuing to invetigate use of the circuit model to obtain solutions of the inverse problem of estimation of conductivity as a function spatial location. 

Statistical Clustering of Gamma Spectra

There is interest in estimation of detection performance (ROC curves) for some gamma spectral measurements in terms of detection performance for other more accessible and distributable spectra.  This can be important when the use of a proprietary detection algorithm is restricted.  We devised a statistical spectral clustering methodology, along with appropriate metrics, suitable for spectra that vary by several orders of magnitude over the spectral energy range.  The clustering methodology allows determination of which spectral shapes are statistically equivalent even though the shapes can be different in low counts regions.  For the same gross counts, all spectral shapes in a cluster have similar detection performance.  As part of this research, we also developed a method for estimation of detection performance for spectral measurements that can be approximated by binary linear combinations (constrained to positive coefficients) of other spectra whose detection performance is known. 

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Recent Publications

  1. I. R. Shokair, M. S. Johnson, R. L. Schmitt, and S. M. Sickafoose, Concept for maritime near-surface surveillance using water Raman scattering," Appl. Opt. 57, 4858-4864 (2018).   https://www.osapublishing.org/ao/abstract.cfm?uri=ao-57-17-4858

  2. I. R. Shokair, "Optimal Detection of SNM Sources for PVT Radiation Portal Monitors," Sandia National Laboratories report SAND2018-1147, January 2018.

  3. I. R. Shokair and D. J. Mitchell, "STR Spectral Grouping Studies," Sandia National Laboratories report SAND2018-0011, January 2018.

  4. P. Marleau, D. Antonio, J. Brennan, J. Helm, and I. Shokair, "Magnetic Induction Sensors for Detecting Anomalous Shielding of Radiological and Nuclear Materials: a Feasibility Study," Sandia National Laboratories report SAND2017-0537, January 2017.

  5. I. R. Shokair and R. Homan, "Classification of Background Suppression Profiles for Low Background RPM Data," Sandia National Laboratories report SAND2016-6676.   https://www.osti.gov/servlets/purl/1465873

  6. I. R. Shokair and R. Homan, "Spatial Radiation Profile Characterization for Detection of Threat-like Sources," Proceedings of the 57-th INMM Meeting, Atlanta, GA, 2016.   http://toc.proceedings.com/32272webtoc.pdf

  7. I. R. Shokair, "Ratio Distributions for Use in Spectral Radiation Detection Algorithms," Sandia National Laboratories report SAND2014-18941, October 2014.   https://prod.sandia.gov/techlib-noauth/access-control.cgi/2014/1418941.pdf

 

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