A Maximum-Likelihood Approach for Localizing and Characterizing Special Nuclear Material with a Dual-Particle Imager.
dc.contributor.author | Polack, John Kyle | |
dc.date.accessioned | 2016-09-13T13:51:43Z | |
dc.date.available | NO_RESTRICTION | |
dc.date.available | 2016-09-13T13:51:43Z | |
dc.date.issued | 2016 | |
dc.date.submitted | ||
dc.identifier.uri | https://hdl.handle.net/2027.42/133291 | |
dc.description.abstract | The threat of nuclear warfare is an ongoing global concern. The reduction of this threat is an international effort that is aided by numerous technologies aimed at the detection, localization, and characterization of special nuclear material (SNM). Radiation imaging systems have a distinct advantage over more traditional systems in that they are designed to provide localized information of the observed the radiation field. Accurate characterization of detected sources requires robust reconstruction algorithms with well characterized uncertainties that are capable of delivering consistently reliable information. This work presents a versatile detection system and powerful reconstruction algorithm that, when combined, are well-suited to meeting many present challenges in the nuclear nonproliferation and treaty verification fields. The detection system, known as the Dual-Particle Imager (DPI), is a single device capable of performing both imaging and spectroscopy with photons and fast neutrons. The reconstruction algorithm leverages maximum-likelihood expectation-maximization (MLEM) and a robust system matrix, simulated in MCNPX-PoliMi, to perform image reconstruction and simultaneously unfold localized energy spectra for each pixel in the image. The localized energy spectra make it possible to analyze multiple detected sources simultaneously and the spectrum unfolding capabilities significantly improve the information available for characterizing the sources. Combining this algorithm with the DPI allows for localized spectra to be computed for photons and neutrons, which is particularly useful for the localization and characterization of SNM. The capabilities of this algorithm have been demonstrated through series of experiments and a detailed analysis of the uncertainties associated with the reconstruction process has been performed. The algorithm was used to analyze two complex environments, measured by the DPI, that each contained an SNM sample in a field of multiple shielded and unshielded sources. An in-depth analysis of the localized photon and neutron spectra show that not only can all present radioactive materials be identified, but also that some determinations can be made regarding the presence and nature of shielding material. The presented results demonstrate the relevance of this work to the nuclear nonproliferation and treaty verification fields | |
dc.language.iso | en_US | |
dc.subject | radiation imaging | |
dc.subject | spectrum unfolding | |
dc.subject | nuclear non-proliferation | |
dc.subject | nuclear safeguards | |
dc.subject | photon | |
dc.subject | neutron | |
dc.title | A Maximum-Likelihood Approach for Localizing and Characterizing Special Nuclear Material with a Dual-Particle Imager. | |
dc.type | Thesis | en_US |
dc.description.thesisdegreename | PhD | |
dc.description.thesisdegreediscipline | Nuclear Engineering and Radiological Sciences | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.contributor.committeemember | Pozzi, Sara A. | |
dc.contributor.committeemember | Fessler, Jeffrey A | |
dc.contributor.committeemember | He, Zhong | |
dc.contributor.committeemember | Flaska, Marek | |
dc.contributor.committeemember | Marleau, Peter | |
dc.subject.hlbsecondlevel | Nuclear Engineering and Radiological Sciences | |
dc.subject.hlbtoplevel | Engineering | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/133291/1/kpolack_1.pdf | |
dc.identifier.orcid | 0000-0003-3114-654X | |
dc.identifier.name-orcid | Polack, John; 0000-0003-3114-654X | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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