Currie detection limits in gamma-ray spectroscopy,

Currie Hypothesis testing is applied to gamma-ray spectral data, where an optimum part of the peak is used and the background is considered well known from nearby channels. With this, the risk of making Type I errors is about 100 times lower than commonly assumed. A programme, PeakMaker, produces random peaks with given characteristics on the screen and calculations are done to facilitate a full use of Poisson statistics in spectrum analyses. Short technical note summary: The Currie decision limit concept applied to spectral data is reinterpreted, which gives better consistency between the selected error risk and the observed error rates. A PeakMaker program is described and the few count problem is analyzed.