Observer models were developed to process data in list-mode format in order to perform binary discrimination tasks for use in an arms-control-treaty context. Data used in this study was generated using GEANT4 Monte Carlo simulations for photons using custom models of plutonium inspection objects and a radiation imaging system. Observer model performance was evaluated and presented using the area under the receiver operating characteristic curve. The ideal observer was studied under both signal-known-exactly conditions and in the presence of unknowns such as object orientation and absolute count-rate variability; when these additional sources of randomness were present, their incorporation into the observer yielded superior performance.
FY2014 technical report of our project funded by DNN R&D that leverages advanced inference methods developed for medical and adaptive imaging to address arms control applications. We seek a method to acquire and analyze imaging data of declared treaty-accountable items without creating an image of those objects or otherwise storing or revealing any classified information. Such a method would avoid the use of classified-information barriers. We present our progress on FY2014 tasks defined in our life-cycle plan. We also describe some future work that is part of the continuation of this project in FY2015 and beyond as part of a venture that joins ours with a related PNNL project.
For fiscal year 2014 this project aimed to look at, characterize, and attempt to offer solutions to a few practical issues a cloud-based RIID system would have to deal with.
Conventional full spectrum gamma spectroscopic analysis has the objective of quantitative identification of all the radionuclides present in a measurement. For low-energy resolution detectors such as NaI, when photopeaks alone are not sufficient for complete isotopic identification, such analysis requires template spectra for all the radionuclides present in the measurement. When many radionuclides are present it is difficult to make the correct identification and this process often requires many attempts to obtain a statistically valid solution by highly skilled spectroscopists. A previous report investigated using the targeted principal component analysis method (TPCA) for detection of embedded sources for RPM applications. This method uses spatial/temporal information from multiple spectral measurements to test the hypothesis of the presence of a target spectrum of interest in these measurements without the need to identify all the other radionuclides present. The previous analysis showed that the TPCA method has significant potential for automated detection of target radionuclides of interest, but did not include the effects of shielding. This report complements the previous analysis by including the effects of spectral distortion due to shielding effects for the same problem of detection of embedded sources. Two examples, one with one target radionuclide and the other with two, show that the TPCA method can successfully detect shielded targets in the presence of many other radionuclides. The shielding parameters are determined as part of the optimization process using interpolation of library spectra that are defined on a 2D grid of atomic numbers and areal densities.