Optimal design of passive gamma-ray spectrometers

Abstract

Passive gamma-ray spectrometers composed of attenuation filters and integrating detector materials provide important advantages in terms of zero-power operation and ruggedness for long-term monitoring scenarios (e.g. national security or environmental remediation). However, the many design parameters, including attenuation filter material and thickness and number of pixels (filter/integrating material combinations), present a challenging optimization problem in designing spectrometers for different applications. In many of these applications, the goal is simply one of anomaly detection deciding that there is a gamma-ray source not normally found in the nuisance source populations of that particular measurement environment. A passive spectrometer design study approach using an anomaly detection metric is presented here, and is founded on "injecting" target sources of interest (e.g. 57Co, 133Ba, 137Cs) into a nuisance source population that represents the widely varying backgrounds typical of long-term monitoring scenarios. The design evaluation metric is quantified by the probability of detection given a required probability of false alarm. A genetic algorithm employs this metric to probe the large design space and identify superior spectrometer designs