Detection of nuclear sources in search survey using dynamic quantum clustering of gamma-ray spectral data
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In a search scenario, nuclear background spectra are continuously measured in short acquisition intervals with a mobile detector-spectrometer. Detecting sources from measured data is difficult because of low signal-to-noise ratio (S/N of spectra, large and highly varying background due to naturally occurring radioactive material (NORM), and line broadening due to limited spectral resolution of nuclear detector. We have invented a method for detection of sources using clustering of spectral data. Our method takes advantage of the physical fact that a source not only produces counts in the region of its spectral emission, but also has the effect on the entire detector spectrum via Compton continuum. This allows characterizing the low S/N spectrum without distinct isotopic lines using multiple data features. We have shown that noisy spectra with low S/N can be grouped by overall spectral shape similarity using a data clustering technique called Dynamic Quantum Clustering (DQC). The spectra in the same cluster can then be averaged to enhance S/N of the isotopic spectral line. This would allow for increased accuracy of isotopic identification and lower false alarm rate. Our method was validated in a proof-of-principle study using a data set of spectra measured in one-second intervals with sodium iodide detector. The data set consisted of over 7000 spectra obtained in urban background measurements, and approximately 70 measurements of 137Cs and 60Co sources. Using DQC analysis, we have observed that all spectra containing 137Cs and 60Co signal cluster away from the background.
KeywordsFalse Alarm Lower False Alarm Rate Nuclear Background Compton Continuum Naturally Occur Radioactive Material
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