Exploring the Impact of Nuclear Data Uncertainties in Ultrahigh Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation
Abstract
High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced Xrays and gammarays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signaltobackground ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and halflives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.
 Authors:
 Los Alamos National Laboratory, Los Alamos, NM (United States)
 Oak Ridge National Laboratory, Oak Ridge, TN (United States)
 Publication Date:
 OSTI Identifier:
 22436773
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Nuclear Data Sheets; Journal Volume: 123; Conference: International workshop on nuclear data covariances, Santa Fe, NM (United States), 28 Apr  1 May 2014; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; APPROXIMATIONS; CALORIMETRY; DATA COVARIANCES; GAMMA DETECTION; GAMMA RADIATION; GAMMA SPECTROSCOPY; HALFLIFE; HIGHPURITY GE DETECTORS; RADIOISOTOPES; RESOLUTION; X RADIATION
Citation Formats
Burr, T., Email: tburr@lanl.gov, Hoover, A., Croft, S., and Rabin, M. Exploring the Impact of Nuclear Data Uncertainties in Ultrahigh Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation. United States: N. p., 2015.
Web. doi:10.1016/J.NDS.2014.12.025.
Burr, T., Email: tburr@lanl.gov, Hoover, A., Croft, S., & Rabin, M. Exploring the Impact of Nuclear Data Uncertainties in Ultrahigh Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation. United States. doi:10.1016/J.NDS.2014.12.025.
Burr, T., Email: tburr@lanl.gov, Hoover, A., Croft, S., and Rabin, M. 2015.
"Exploring the Impact of Nuclear Data Uncertainties in Ultrahigh Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation". United States.
doi:10.1016/J.NDS.2014.12.025.
@article{osti_22436773,
title = {Exploring the Impact of Nuclear Data Uncertainties in Ultrahigh Resolution Gamma Spectroscopy for Isotopic Analysis Using Approximate Bayesian Computation},
author = {Burr, T., Email: tburr@lanl.gov and Hoover, A. and Croft, S. and Rabin, M.},
abstractNote = {High purity germanium (HPGe) currently provides the highest readily available resolution gamma detection for a broad range of radiation measurements, but microcalorimetry is a developing option that has considerably higher resolution even than HPGe. Superior microcalorimetry resolution offers the potential to better distinguish closely spaced Xrays and gammarays, a common challenge for the low energy spectral region near 100 keV from special nuclear materials, and the higher signaltobackground ratio also confers an advantage in detection limit. As microcalorimetry continues to develop, it is timely to assess the impact of uncertainties in detector and item response functions and in basic nuclear data, such as branching ratios and halflives, used to interpret spectra in terms of the contributory radioactive isotopes. We illustrate that a new inference option known as approximate Bayesian computation (ABC) is effective and convenient both for isotopic inference and for uncertainty quantification for microcalorimetry. The ABC approach opens a pathway to new and more powerful implementations for practical applications than currently available.},
doi = {10.1016/J.NDS.2014.12.025},
journal = {Nuclear Data Sheets},
number = ,
volume = 123,
place = {United States},
year = 2015,
month = 1
}

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