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

Abstract

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.

Design and development of a detector system and software tool installed on a mobile laboratory to provide both radiological characterisation of large areas and a simple tool for localising radioactive sources

“…More recently, Bukartas et al [11,12] proposed a Bayesian method for locating lost gamma sources. The method can estimate the source activity within 50% of its actual activity and its location within 27% uncertainty.…”