Optimization of Spectral String Data Analysis Using a Binomial Discriminator for Weak-source Detection Decisions

Abstract

Operational health physics applications, such as radiological and nuclear monitoring and detection for homeland security or radiation protection purposes, generate time sequences of independent individual measurement data. Statistical algorithms have been developed that use the analysis of patterns in the data strings to enhance the test statistic for the decision on the absence or presence of a radiation source. These hypothesis test procedures have been applied to spectral data and have been optimized for the highest rate of correct identification of a weak Cs source at constant false positive detection rates. Optimization of correct detection decisions was investigated for various string data sequence lengths and for the regions of interest in the gamma spectrum. The highest correct source identification is achieved for string data analyses of the spectral contributions that maximize a [INCREMENT]μ/σ criterion, including energy regions around and containing the photopeak, but potentially also regions in the gamma spectrum other than those photopeak energies.