Energy Calibration Algorithms for the Multiple Isotope Material Basis Set (MIMBS) Isotope Identification Method

Abstract

As we show in this report, the multiple isotope material basis set (MIMBS) method for isotope identification with medium- and low-resolution gamma-ray detectors requires an accurate energy calibration to be optimally effective. Determination of the detector's energy calibration using one or more known radioisotopes is generally required for all applications. We are developing an algorithm that automatically finds the best energy calibration using one or more identified (standard) gamma spectra as input, with no effort on the part of the user other than qualitatively identifying the isotopes present in the spectrum. Instruments that suffer significant energy calibration drift, such as Nal handheld identifiers, also require some type of real time gain stabilization to keep their calibration steady. We have developed two approaches for stabilizing the energy calibration for MIMBS analyses, (1) a standard peak-based gain stabilization algorithm for use when seeded or background gamma peaks are known to be present and (2) a peak-free stabilization algorithm based on a non-linear gain optimization. In this report, we describe the calibration algorithms and evaluate their effectiveness on real and simulated gamma-ray spectra under varying measurement conditions.