Improved uncertainty quantification in nondestructive assay for nonproliferation

Burr, Tom; Croft, Stuart; Jarman, Kenneth D.; Nicholson, Andrew; Norman, Claude; Walsh, Stephen J.

doi:10.1016/j.chemolab.2016.10.007

Cited by 9 publications

(25 citation statements)

References 28 publications

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“…Both inverse and reverse calibrations involve ratios of random variables, which can be problematic [7,17]. In inverse calibration, the solution in (4) involves division by the random variablê1, which has a normal distribution under typical modeling assumptions.…”

Section: Extension Of Standard Regression Results To Calibrationmentioning

confidence: 99%

“…where denotes the expected value (i.e., the first moment of the underlying probability distribution) [7]. (7) in the context of NM measurements by gamma detection [7,17] and neutron detection [1,7,23,24].…”

Section: /( − 1)mentioning

confidence: 99%

“…The GUM also briefly mentions top-down UQ in the context of applying analysis of variance (ANOVA, [4]) to data from interlaboratory studies. Although the GUM is useful, it is being revised because it has known limitations [5][6][7]. For example, the GUM provides little technical guidance regarding calibration as a type of bottom-up UQ or regarding 2 Science and Technology of Nuclear Installations top-down UQ [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, random error variance estimates (from paired data) are based on Grubbs' estimator or variations of Grubbs' estimator, which was originally developed by Grubbs to estimate random error variance separately for each of the two methods applied to each of several items, without repetition of measurement by either method [13,14]. In Section 3, Grubbs' estimator, constrained versions of Grubbs' estimator, and a Bayesian alternative [7] are described; the Bayesian option easily allows for parameter constraints and prior information regarding the relative magnitudes of variance components to be exploited to improve top-down UQ.…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 provides a background on bottom-up UQ for NDA, describes a gammabased NDA example and a neutron-based NDA example, and illustrates why simulation is necessary for improved UQ for calibration data. Section 3 reviews currently used top-down UQ and describes a new Bayesian option [7] that applies approximate Bayesian computation. Section 4 provides a new simulation study assessing the sensitivity of estimated NM loss detection probabilities to estimation errors in measurement error RSDs.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Uncertainty Quantification in Nuclear Safeguards

Bonner

Burr

Krieger

et al. 2017

Science and Technology of Nuclear Installations

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Nuclear safeguards aim to confirm that nuclear materials and activities are used for peaceful purposes. To ensure that States are honoring their safeguards obligations, quantitative conclusions regarding nuclear material inventories and transfers are needed. Statistical analyses used to support these conclusions require uncertainty quantification (UQ), usually by estimating the relative standard deviation (RSD) in random and systematic errors associated with each measurement method. This paper has two main components. First, it reviews why UQ is needed in nuclear safeguards and examines recent efforts to improve both top-down (empirical) UQ and bottom-up (first-principles) UQ for calibration data. Second, simulation is used to evaluate the impact of uncertainty in measurement error RSDs on estimated nuclear material loss detection probabilities in sequences of measured material balances.

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Section: Extension Of Standard Regression Results To Calibrationmentioning

confidence: 99%