Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
Abstract
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.
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1678769
- Grant/Contract Number:
- US DOE 89233218CNA000001
- Resource Type:
- Published Article
- Journal Name:
- Algorithms
- Additional Journal Information:
- Journal Name: Algorithms Journal Volume: 13 Journal Issue: 10; Journal ID: ISSN 1999-4893
- Publisher:
- MDPI AG
- Country of Publication:
- Switzerland
- Language:
- English
Citation Formats
Burr, Tom, Favalli, Andrea, Lombardi, Marcie, and Stinnett, Jacob. Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy. Switzerland: N. p., 2020.
Web. doi:10.3390/a13100265.
Burr, Tom, Favalli, Andrea, Lombardi, Marcie, & Stinnett, Jacob. Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy. Switzerland. https://doi.org/10.3390/a13100265
Burr, Tom, Favalli, Andrea, Lombardi, Marcie, and Stinnett, Jacob. Mon .
"Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy". Switzerland. https://doi.org/10.3390/a13100265.
@article{osti_1678769,
title = {Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy},
author = {Burr, Tom and Favalli, Andrea and Lombardi, Marcie and Stinnett, Jacob},
abstractNote = {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.},
doi = {10.3390/a13100265},
journal = {Algorithms},
number = 10,
volume = 13,
place = {Switzerland},
year = {Mon Oct 19 00:00:00 EDT 2020},
month = {Mon Oct 19 00:00:00 EDT 2020}
}
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