Accuracy of a Bayesian technique to estimate position and activity of orphan gamma-ray sources by mobile gamma spectrometry: Influence of imprecisions in positioning systems and computational approximations.

The purpose of this study was to investigate the effects of experimental data on performance of a developed Bayesian algorithm tailored for orphan source search, estimating which parameters affect the accuracy of the algorithm. The algorithm can estimate the position and activity of a gamma-ray point source from experimental mobile gamma spectrometry data. Bayesian estimates were made for source position and activity using mobile gamma spectrometry data obtained from one 123% HPGe detector and two 4-l NaI(Tl) detectors, considering angular variations in counting efficiency for each detector. The data were obtained while driving at 50 km/h speed past the sources using 1 s acquisition interval in the detectors. It was found that deviations in the recorded coordinates of the measurements can potentially increase the uncertainty in the position of the source 2 to 3 times and slightly decrease the activity estimations by about 7%. Due to the various sources of uncertainty affecting the experimental data, the maximum predicted relative deviations of the activity and position of the source remained about 30% regardless of the signal-to-noise ratio of the data. It was also found for the used vehicle speed of 50 km/h and 1 s acquisition time, that if the distance to the source is greater than the distance travelled by the detector during the acquisition time, it is possible to use point approximations of the count-rate function in the Bayesian likelihood with minimal deviations from the integrated estimates of the count-rate function. This approximation reduces the computational demands of the algorithm increasing the potential for applying this method in real-time orphan source search missions.

Maximum detection distances for gamma emitting point sources in mobile gamma spectrometry.

In this study an algorithm was developed for calculating maximum detectable distance (MDD) for mobile gamma-ray detection of lost sources in-situ for some common mobile spectrometer systems and gamma sources (137Cs and 60Co). The MDD is a function of detector efficiency, vehicle speed, acquisition time interval, radiation background level and accepted frequency of false alarms. To test its accuracy in predicting the MDD experiments were conducted in-situ. Results indicate agreement with experimental mobile detection distance measurements for 60Co and 137Cs point sources. Both the algorithm and the field test show that at a vehicle speed of 50 km/h, the optimal acquisition time intervals range between 3 and 10 s when searching for unshielded sources with activities of 137Cs and 60Co in the order of 100 MBq. Longer acquisition time intervals (20-30 s) are better to detect higher activity sources at the same speed. However, at higher speeds, shorter time intervals should be selected to increase the MDD. The developed algorithm can help select the optimal combination of detectors, vehicle speed, and instrument settings when using mobile searching in the event of loss of gamma-ray sources.

Bayesian algorithm to estimate position and activity of an orphan gamma source utilizing multiple detectors in a mobile gamma spectrometry system.

To avoid harm to the public and the environment, lost ionizing radiation sources must be found and brought back under the regulatory control as soon as possible. Usually, mobile gamma spectrometry systems are used in such search missions. It is possible to estimate the position and activity of point gamma sources by performing Bayesian inference on the measurement data. The aim of this study was to theoretically investigate the improvements in the Bayesian estimations of the position and activity of a point gamma source due to introduction of data from multiple detectors with angular variations of efficiency. Three detector combinations were tested-a single 123% HPGe detector, single 4l NaI (Tl) detector and a 123% HPGe with 2x4l NaI (Tl) detector combination-with and without angular efficiency variations for each combination resulting in six different variants of the Bayesian algorithm. It was found that introduction of angular efficiency variations of the detectors did improve the accuracy of activity estimation slightly, while introduction of data from additional detectors lowered the signal-to-noise ratio threshold of the system significantly, increasing the stability and accuracy of the estimated source position and activity, for a given signal-to-noise ratio.