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Assessment of Novel Techniques for Nuclear Data Evaluation
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
XCP Division, Los Alamos National Lab, NM, USA.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
Department of Statistics and Actuarial Science, Simon Fraser University, Canada.
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2017 (English)Conference paper (Refereed)
Abstract [en]

The quality of evaluated nuclear data can be impacted by, e.g., the choice of the evaluation algorithm. The objective of this work is to compare the performance of the evaluation techniques GLS, GLS-P, UMC-G, and, UMC-B, by using synthetic data. In particular, the effects of model defects are investigated. For small model defects, UMC-B and GLS-P are found to perform best, while these techniques yield the worst results for a significantly defective model; in particular, they seriously underestimate the uncertainties. If UMC-B is augmented with Gaussian processes,it performs distinctly better for a defective model but is more susceptible to an inadequate experimental covariance estimate.

Place, publisher, year, edition, pages
2017.
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-322558OAI: oai:DiVA.org:uu-322558DiVA: diva2:1098683
Conference
16th International Symposium of Reactor Dosimetry (ISRD16)
Note

Oral presentation at conference, submitted paper in review process.

Available from: 2017-05-25 Created: 2017-05-25 Last updated: 2017-05-25

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