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 UniversityRadiation Protection Bureau, Health Canada

Goal and MethodologyGoal: 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.

Gamma-Ray Spectroscopy (Wikipedia)

The gamma-ray spectrum of natural uranium, showing about a dozen discrete lines superimposed on a smooth continuum, allows the identification the nuclides 226Ra, 214Pb, and 214Bi of the uranium decay chain.

The quantitative study of theEnergy spectra of gamma-ray Sources.

Most radioactive sources produce gamma rays ofvarious energy levels and intensities

The data

I= Iodine, Tc=Technicium, Th= Thallium, Cs=Cesium, Co=Cobalt

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: 24,712/6Data 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 imbalanceIssue 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.

The effect of rain

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.

Approach (cont’d)

Experiments

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

DistanceWinner: Mahalanobis Distance

Experiments (Cont’d)

Results

Conclusions and report on further experiments