Applying a Template of Expected Uncertainties to Updating 239Pu(n,f) Cross-section Covariances in the Neutron Data Standards Database

Neudecker, Denise; Smith, Donald L.; Tôvesson, F.; Capote, R.; White, Morgan Curtis; Bowden, N. S.; Snyder, L.; Carlson, A.D.; Casperson, R. J.; Pronyaev, V.G.; Sangiorgio, S.; Schmitt, K. T.; Seilhan, B.; Walsh, N.; Younes, W.

doi:10.1016/j.nds.2019.12.005

Cited by 27 publications

(26 citation statements)

References 52 publications

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“…The possibility to get posterior estimates and uncertainties of all quantities involved, such as the various systematic error components, helps to quickly identify problematic modeling assumptions by checking the compatibility of the posterior estimates with the prior estimates and uncertainties. At some point in the future, templates of measurement uncertainties [96,97] may be used for the specification of default priors on the experimental errors associated with different measurement types, and also to check the compatibility of the posterior estimates with sensible ranges given by a template. The Bayesian network framework is also compatible with the procedures described in [14,22,98,99] for the automated determination of missing or misspecified uncertainties.…”

Section: Discussionmentioning

confidence: 99%

Nuclear data evaluation with Bayesian networks

Schnabel¹,

Capote²,

Koning³

et al. 2021

Preprint

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Bayesian networks are graphical models to represent the deterministic and probabilistic relationships between variables within the Bayesian framework. The knowledge of all variables can be updated using new information about some of the variables. The Bayesian Generalized Linear Least Squares method can be regarded as an inference method for Bayesian networks of variables with multivariate normal priors and linear relationships between them. We show that relying explicitly on the Bayesian network interpretation enables large scale inference and gives more flexibility in incorporating prior assumptions and constraints into the nuclear data evaluation process, such as the constraints that some cross sections equal linear combinations of other cross sections and that all cross sections must be non-negative. The latter constraint is accounted for by a nonlinear transformation and therefore we also discuss inference in Bayesian networks with non-linear relationships between variables. Using Bayesian networks, the evaluation process yields more detailed information, such as posterior estimates and uncertainties of all statistical and systematic errors associated with the experiments. We further elaborate on a sparse Gaussian process construction that can be well integrated into the Bayesian network framework and applied to, e.g., the modeling of energy-dependent model parameters, model deficiencies of the physics model or energy-dependent systematic errors of experiments. We present three proof-of-concept examples that emerged in the context of the neutron data standards project and in the ongoing international evaluation efforts of 56 Fe. In the first example we demonstrate the modelization and explicit estimation of relative energy-dependent error components associated with experimental datasets. Then we show that Bayesian networks in combination with the outlined Gaussian process construction may be applied to an evaluation of 56 Fe in the energy range between one and two MeV, where it is difficult to obtain satisfactory evaluations by R-Matrix and nuclear model fits. Finally, we present a model-based evaluation of 56 Fe between 5 MeV and 30 MeV with a consistent and statistically sound treatment of model deficiencies. The R scripts to reproduce the Bayesian network examples and the nucdataBaynet package for Bayesian network modeling and inference have been made publicly available.

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Section: Discussionmentioning

confidence: 99%

Nuclear data evaluation with Bayesian networks

Schnabel¹,

Capote²,

Koning³

et al. 2021

Preprint

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“…Ref. [6,7,8,9,10,11,12,13,14,13,14,15,16,17,18,19,20,21,22,23,24,25]. I am happy to report that I am satisfied with the level of development of technical skill that I was afforded to achieve this summer.…”

Section: Description Of the Research Projectmentioning

confidence: 97%

Modeling Systematic Discrepancies in Nuclear-Data Measurements with Machine Learning

Michael

2020

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Nuclear data and their associated co-variances are constantly being reevaluated as techniques improve and as new experimental data, as well as nuclearmodel developments, emerge. A standard technique used to evaluate mean values in nuclear data, and their associated covariances, is the generalized linear least squares (GLLS) method. Aligning with recent efforts to incorporate measurement features into nuclear data evaluation, we augment GLLS by including a linear term which attempts to predict potential systematic discrepancies in experimental data as related to the measurement features. Due to the general nature of this augmentation, we are able to apply this evaluation to three key observables: neutroninduced fission cross sections, the average prompt neutron multiplicity, and the prompt-fission neutron spectrum of 239 Pu.

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“…Uranium deposits are typically vapor deposited in the form of UF 4 while plutonium deposits are often electroplated. The vapor deposits are smooth and uniform while the electroplated deposits can be subject to surface roughness, flaking and non-uniformities [4,31]. Corrections for the energy dependent detector efficiency resulting from fission frag-ment anisotropy were developed by G. Carlson [32].…”

Section: Rotation Validationmentioning

confidence: 99%

“…Neudecker et al [4] systematically reviewed previous measurements of the 239 Pu(n,f) cross section and developed a template to estimate unreported uncertainties for (n,f) measurements and to provide a guide for future measurements to follow. Tovesson [5] detailed the uncertainty quantification needed for fission cross-section measurements taken specifically at the Los Alamos Neutron Science Center (LANSCE), where the measurements presented in this paper were made.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of the $^{239}$Pu(n,f)/$^{235}$U(n,f) Cross-Section Ratio with the NIFFTE fission Time Projection Chamber

Snyder

Anastasiou²,

Bowden³

et al. 2021

Preprint

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The 239 Pu(n,f)/ 235 U(n,f) cross-section ratio has been measured with the fission Time Projection Chamber (fissionTPC) from 100 keV to 100 MeV. The fissionTPC provides three-dimensional reconstruction of fission-fragment ionization profiles, allowing for a precise quantification of measurement uncertainties. The measurement was performed at the Los Alamos Neutron Science Center which provides a pulsed white source of neutrons. The data are recommended to be used as a cross-section ratio shape. A discussion of the status of the absolute normalization and comparisons to ENDF evaluations and previous measurements is included.

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Applying a Template of Expected Uncertainties to Updating 239Pu(n,f) Cross-section Covariances in the Neutron Data Standards Database

Cited by 27 publications

References 52 publications

Nuclear data evaluation with Bayesian networks

Nuclear data evaluation with Bayesian networks

Modeling Systematic Discrepancies in Nuclear-Data Measurements with Machine Learning

Measurement of the $^{239}$Pu(n,f)/$^{235}$U(n,f) Cross-Section Ratio with the NIFFTE fission Time Projection Chamber

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