Neutron detection systems utilize statistical alarm techniques where a measured false alarm rate (FAR) can vary drastically from the FAR predicted by a theoretical model. The ability to set an alarm threshold that results in a practically controlled FAR is crucial to characterize detector sensitivity with both accuracy and precision. A generalized and automated method is presented to statistically evaluate FAR performance by assuming that the FAR itself is not deterministic, but a normal stochastic process over a specific parameter to be corrected that will hereafter be referred to as the correction. In this manner, a specific correction results in not only a point estimate of FAR, but also a confidence interval. The central objective is focused exclusively on characterization assuming that experiments are executed in a tightly controlled environment so that an accurate comparison is enabled across detectors. Once a correction is calculated, the estimated FAR is only assumed accurate in a similar environment for sensitivity evaluation. Initially, the calculated correction factor was used to compare FARs across various distributions including normal, corrected normal, Poisson, and a simplified normal distribution. Later verification data sets were used to empirically demonstrate the rate of containment of measured confidence coefficients using two detectors of different technology. A second application uses the correction method to improve the signal-to-noise ratio metric to agree more with dynamic sensitivity results. Finally, a third application studies the effect of altering the duration of background acquisition on FAR performance.
Review of current neutron detection systems for emergency response
