2018
DOI: 10.1051/epjn/2018032
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Abstract: In the last decade or so, estimating uncertainties associated with nuclear data has become an almost mandatory step in any new nuclear data evaluation. The mathematics needed to infer such estimates look deceptively simple, masking the hidden complexities due to imprecise and contradictory experimental data and natural limitations of simplified physics models. Through examples of evaluated covariance matrices for the soon-to-be-released U.S. ENDF/B-VIII.0 library, e.g., cross sections, spectrum, multiplicity, … Show more

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Cited by 4 publications
(4 citation statements)
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References 26 publications
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“…As a consequence, we further demonstrate that first-order perturbation theory is appropriate for the particular subcritical assembly we analyzed. Bayesian inference is used to perform cross section evaluation and validation [8,3,54] and was recently utilized by Evans [12] in optimally adjusting the cross sections to a subcritical experiment. The application of an EKF to NMC experiments for optimal cross section adjustment is novel, however, and is shown in this dissertation to produce best-estimate cross sections such that NMC experiments are more accurately simulated with reduced uncertainty.…”
Section: Novel Contributionsmentioning
confidence: 99%
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“…As a consequence, we further demonstrate that first-order perturbation theory is appropriate for the particular subcritical assembly we analyzed. Bayesian inference is used to perform cross section evaluation and validation [8,3,54] and was recently utilized by Evans [12] in optimally adjusting the cross sections to a subcritical experiment. The application of an EKF to NMC experiments for optimal cross section adjustment is novel, however, and is shown in this dissertation to produce best-estimate cross sections such that NMC experiments are more accurately simulated with reduced uncertainty.…”
Section: Novel Contributionsmentioning
confidence: 99%
“…If the uncertainty in the measurement parameter itself is known (or if a reasonable estimate may be obtained), the measured response covariance may be computed using linear propagation of uncertainty, Uncertainty in the cross sections and the covariance between them comes from uncertainty in the various reaction-rate measurements across several energy regions used to evaluate them [54]. Similar to the measurement parameters, the relative cross section covariance relcov(α, α) may be propagated through the simulated detector response using linear propagation of uncertainty, the BeRP ball in bare and polyethylene-reflected configurations.…”
Section: Propagation Of Uncertainty Through the Detector Response Mommentioning
confidence: 99%
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