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Anomaly Detection in Gamma Ray Spectra: A Machine Learning Perspective

Anomaly Detection in Gamma Ray Spectra: A Machine Learning Perspective. Nathalie Japkowicz , Colin Bellinger , Shiven Sharma, Rodney Berg, Kurt Ungar University of Ottawa, Northern Illinois University Radiation Protection Bureau, Health Canada. Goal and Methodology.

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Anomaly Detection in Gamma Ray Spectra: A Machine Learning Perspective

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  1. Anomaly Detection in Gamma Ray Spectra: A Machine Learning Perspective Nathalie Japkowicz, Colin Bellinger, Shiven Sharma, Rodney Berg, Kurt Ungar University of Ottawa, Northern Illinois University Radiation Protection Bureau, Health Canada

  2. Goal and Methodology • Goal: To identify people concealing radioactive material that may represent a threat to attendees at public gatherings. • Methodology:Analysis of Gamma-Ray spectra produced by spectrometer s at short intervals of time and decision on the fly of whether a threat is present. • General idea:to place spectrometers in strategic locations (e.g., the entry points to the event) and try to detect whether the new spectra coming in are similar or different from a normal spectrum for this particular location.

  3. Gamma-Ray Spectroscopy (Wikipedia) The quantitative study of the Energy spectra of gamma-ray Sources. Most radioactive sources produce gamma rays of various energy levels and intensities The gamma-ray spectrum of natural uranium, showing about a dozen discrete lines superimposed on a smooth continuum, allows the identification the nuclides226Ra, 214Pb, and 214Bi of the uranium decay chain.

  4. The data I= Iodine, Tc=Technicium, Th= Thallium, Cs=Cesium, Co=Cobalt

  5. Approach • To apply Machine Learning/Pattern recognition techniques to the data. • Issue 1: There is a lot of background data, but very few alarms. E.g., for one station: 24,712/6 • Data was augmented with simulated Cobalt entries(though we only used that data for testing) • We used one-class learning/anomaly detection algorithms to deal with this extreme class imbalance • Issue 2: We discovered that rain was a problem as it masked the presence of isotopes in the spectra. • Since we had labelled data of both the rain and non-rain classes, we used binary classification on this problem.

  6. The effect of rain

  7. Hypothesis Separating rain from non-rain data in a first phase and applying an anomaly detection system on each group of data separately in a second phase could help us improve the results.

  8. Approach (cont’d)

  9. Experiments

  10. Experiments (Cont’d) • We experimented with different classifiers in both phases. • Phase 1: • Classifiers tried: SVM, J48, NB, MLP and IBL. • Winner: NB • Phase 2: • Classifiers tried: oc-SVM, AA, Mahalanobis Distance • Winner: Mahalanobis Distance

  11. Experiments (Cont’d)

  12. Results

  13. Conclusions and report on further experiments

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