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Treating model defects by fitting smoothly varying model parameters: energy dependence in nuclear data evaluation
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions)
(English)In: Review of Scientific Instruments, ISSN 0034-6748, E-ISSN 1089-7623Article in journal (Refereed) Submitted
Abstract [en]

The fitting of models to data is essential in nuclear data evaluation, as in many other fields of science. The models maybe necessary for interpolation or extrapolation, but they are seldom perfect; there are model defects present which can result in severe biases and underestimated uncertainties. This work presents and investigates the idea to treat this problem by letting the model parameters vary smoothly with an input parameter. To be specific, the model parameters for neutron cross sections are allowed to vary with neutron energy. The parameter variation is limited by Gaussian processes, but the method should not be confused with adding a Gaussian process to the model. The performance of the method is studied using a large number of synthetic data sets, such that it is possible to quantitatively study the distribution of results compared to the underlying truth. There are imperfections in the results, but the method is seen to readily outperform fits without the energy dependent parameters. In particular, the estimates of uncertainty and correlations are much better. Hence, the method seems to offer a promising route for future treatment of model defects, both for nuclear data and elsewhere.

National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-348552OAI: oai:DiVA.org:uu-348552DiVA, id: diva2:1197899
Available from: 2018-04-16 Created: 2018-04-16 Last updated: 2018-04-16
In thesis
1. Approaching well-founded comprehensive nuclear data uncertainties: Fitting imperfect models to imperfect data
Open this publication in new window or tab >>Approaching well-founded comprehensive nuclear data uncertainties: Fitting imperfect models to imperfect data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Nuclear physics has a wide range of applications; e.g., low-carbon energy production, medical treatments, and non-proliferation of nuclear weapons. Nuclear data (ND) constitute necessary input to computations needed within all these applications.

This thesis considers uncertainties in ND and their propagation to applications such as ma- terial damage in nuclear reactors. TENDL is today the most comprehensive library of evaluated ND (a combination of experimental ND and physical models), and it contains uncertainty estimates for all nuclides it contains; however, TENDL relies on an automatized process which, so far, includes a few practical remedies which are not statistically well-founded. A longterm goal of the thesis is to provide methods which make these comprehensive uncertainties well-founded. One of the main topics of the thesis is an automatic construction of experimental covariances; at first by attempting to complete the available uncertainty information using a set of simple rules. The thesis also investigates using the distribution of the data; this yields promising results, and the two approaches may be combined in future work.

In one of the papers underlying the thesis, there are also manual analyses of experiments, for the thermal cross sections of Ni-59 (important for material damage). Based on this, uncertainty components in the experiments are sampled, resulting in a distribution of thermal cross sections. After being combined with other types of ND in a novel way, the distribution is propagated both to an application, and to an evaluated ND file, now part of the ND library JEFF 3.3.

The thesis also compares a set of different techniques used to fit models in ND evaluation. For example, it is quantified how sensitive different techniques are to a model defect, i.e., the inability of the model to reproduce the truth underlying the data. All techniques are affected, but techniques fitting model parameters directly (such as the primary method used for TENDL) are more sensitive to model defects. There are also advantages with these methods, such as physical consistency and the possibility to build up a framework such as that of TENDL.

The treatment of these model defects is another main topic of the thesis. To this end, two ways of using Gaussian processes (GPs) are studied, applied to quite different situations. First, the addition of a GP to the model is used to enable the fitting of arbitrarily shaped peaks in a histogram of data. This is shown to give a substantial improvement compared to if the peaks are assumed to be Gaussian (when they are not), both using synthetic and authentic data.

The other approach uses GPs to fit smoothly energy-dependent model parameters in an ND evaluation context. Such an approach would be relatively easy to incorporate into the TENDL framework, and ensures a certain level of physical consistency. It is used on a TALYS-like model with synthetic data, and clearly outperforms fits without the energy-dependent model parameters, showing that the method can provide a viable route to improved ND evaluation. As a proof of concept, it is also used with authentic TALYS, and with authentic data.

To conclude, the thesis takes significant steps towards well-founded comprehensive ND un- certainties.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 119
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1669
Keywords
Evaluated nuclear data, uncertainty propagation, uncertainty quantification, model defects, Gaussian processes, TALYS, TENDL, covariances.
National Category
Subatomic Physics
Research subject
Physics with specialization in Applied Nuclear Physics
Identifiers
urn:nbn:se:uu:diva-348553 (URN)978-91-513-0334-5 (ISBN)
Public defence
2018-06-08, Häggsalen, Ångströmslaboratoriet, Lägerhyddsv. 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2018-05-17 Created: 2018-04-16 Last updated: 2018-05-17

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