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Integral adjustment of nuclear data libraries: finding unrecognized systematic uncertainties and correlations
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0002-7595-8024
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0001-5007-2975
Laboratory for Reactor Physics Systems Behaviour, Paul Scherrer Institut, Villigen, Switzerland.
2019 (English)In: Conference program & Abstract book: 2019 International Conference on Nuclear Data for Science and Technology, May 19-24, 2019, Beijing, China, 2019, p. 212-212Conference paper, Oral presentation with published abstract (Other academic)
Abstract [sv]

To reduce the uncertainties and obtain a better predictive power, integral adjustment of nuclear data libraries is one powerful option. Databases with integral experiments, such as the ICSBEP contain a large amount of data. When adjusting nuclear data using these integral experiments, it is important to not only include reported experimental uncertainties but also to account for the possibility of unreported experimental uncertainties and correlations between experiments, and calculation uncertainties. Unreported uncertainties and correlations can be identified and possibly quantified using marginal likelihood optimization (MLO). MLO has previously been tested for integral adjustment. In this paper, a method for including more information from the full likelihood space is pursued. It is shown that MLO can be an effective tool in addressing unknown uncertainties and correlations for a selected number of integral experiments. Results in terms of obtained parameter estimates as well as of posterior uncertainties and correlations are reported. The results are validated against an independent set of integral experiments. The findings are important for large-scale ND evaluations that heavily rely on automatization, such as TENDL, but also for any integral adjustment where a complete knowledge of all uncertainty components is out of reach. The authors believe that this is always the case.

Place, publisher, year, edition, pages
2019. p. 212-212
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-396767OAI: oai:DiVA.org:uu-396767DiVA, id: diva2:1368919
Conference
2019 International Conference on Nuclear Data for Science and Technology, May 19-24, 2019, Beijing, China
Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-11Bibliographically approved

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