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Open AccessArticle

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

1
Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2
Safeguards Science and Technology, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
3
Advanced Nuclear Technology, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(10), 265; https://doi.org/10.3390/a13100265
Received: 21 August 2020 / Revised: 25 September 2020 / Accepted: 14 October 2020 / Published: 19 October 2020
(This article belongs to the Special Issue 2020 Selected Papers from Algorithms Editorial Board Members)
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. View Full-Text
Keywords: gamma spectroscopy; peak location; approximate Bayesian computation; radioisotope identification gamma spectroscopy; peak location; approximate Bayesian computation; radioisotope identification
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Burr, T.; Favalli, A.; Lombardi, M.; Stinnett, J. Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy. Algorithms 2020, 13, 265.

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