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In a search scenario, nuclear background spectra are continuously measured inshort acquisition intervals with a mobile detector-spectrometer. Detectingsources from measured data is difficult because of low signal to noise ratio(S/N) of spectra, large and highly varying background due to naturallyoccurring radioactive material (NORM), and line broadening due to limitedspectral resolution of nuclear detector. We have invented a method fordetection of sources using clustering of spectral data. Our method takesadvantage of the physical fact that a source not only produces counts in theregion of its spectral emission, but also has the effect on the entire detectorspectrum via Compton continuum. This allows characterizing the low S/N spectrumwithout distinct isotopic lines using multiple data features. We have shownthat noisy spectra with low S/N can be grouped by overall spectral shapesimilarity using a data clustering technique called Dynamic Quantum Clustering(DQC). The spectra in the same cluster can then be averaged to enhance S/N ofthe isotopic spectral line. This would allow for increased accuracy of isotopicidentification and lower false alarm rate. Our method was validated in aproof-of principle study using a data set of spectra measured in one-secondintervals with Sodium Iodide detector. The data set consisted of over 7000spectra obtained in urban background measurements, and approximately 70measurements of Cs-137 and Co-60 sources. Using DQC analysis, we have observedthat all spectra containing Cs-137 and Co-60 signal cluster away from thebackground.

Detection of Nuclear Sources in Search Applications using Dynamic Quantum Clustering of Spectral Data