Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Burr, Tom; Favalli, Andrea; Lombardi, Marcie; Stinnett, Jacob

doi:10.3390/a13100265

Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy

Journal Article · Mon Oct 19 00:00:00 EDT 2020 · Algorithms

DOI:https://doi.org/10.3390/a13100265· OSTI ID:1678769

Burr, Tom; Favalli, Andrea; Lombardi, Marcie; Stinnett, Jacob

Radioisotope identification (RIID) algorithms for gamma-ray spectroscopy aim to infer what isotopes are present and in what amounts in test items. RIID algorithms either use all energy channels in the analysis region or only energy channels in and near identified peaks. Because many RIID algorithms rely on locating peaks and estimating each peak’s net area, peak location and peak area estimation algorithms continue to be developed for gamma-ray spectroscopy. This paper shows that approximate Bayesian computation (ABC) can be effective for peak location and area estimation. Algorithms to locate peaks can be applied to raw or smoothed data, and among several smoothing options, the iterative bias reduction algorithm (IBR) is recommended; the use of IBR with ABC is shown to potentially reduce uncertainty in peak location estimation. Extracted peak locations and areas can then be used as summary statistics in a new ABC-based RIID. ABC allows for easy experimentation with candidate summary statistics such as goodness-of-fit scores and peak areas that are extracted from relatively high dimensional gamma spectra with photopeaks (1024 or more energy channels) consisting of count rates versus energy for a large number of gamma energies.

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Sponsoring Organization:: USDOE

OSTI ID:: 1678769

Journal Information:: Algorithms, Journal Name: Algorithms Journal Issue: 10 Vol. 13; ISSN ALGOCH; ISSN 1999-4893

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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