Maximum Likelihood Spectrum Decomposition for Isotope Identification and Quantification
Abstract
A spectral decomposition method has been implemented to identify and quantify isotopic source terms in high-resolution gamma-ray spectroscopy in static geometry and shielding scenarios. Monte-Carlo simulations were used to build the response matrix of a shielded high purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared to decay corrected values from the calibration certificate. For most nuclei in the standard (Am, Cd, Cs, and Co) the deviations from the certificate values were generally no more than \% with a few outliers as high as \%. For Co, the radionuclide with the lowest activity, the deviations from the standard reached as high as \%, driven by the meager statistics in the calibration spectra. Additionally, a complete treatment of error propagation for the technique is presented.
Cite
@article{arxiv.2107.12157,
title = {Maximum Likelihood Spectrum Decomposition for Isotope Identification and Quantification},
author = {J. T. Matta and A. J. Rowe and M. P. Dion and M. J. Willis and A. D. Nicholson and D. E. Archer and H. H. Wightman},
journal= {arXiv preprint arXiv:2107.12157},
year = {2022}
}
Comments
Soon to be published in: IEEE Transactions on Nuclear Science