Unrecognized Sources of Uncertainties (USU) in Experimental Nuclear Data
Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested cau...
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| Published in | Nuclear data sheets Vol. 163; no. C; pp. 191 - 227 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.01.2020
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty (USU) that limit the accuracy to which these physical quantities can be determined.
The objective of our work has been to develop qualitative and quantitative procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU. It then describes numerical procedures we have introduced to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Several realistic examples are described here to illustrate these procedures and demonstrate their outcomes and limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community. |
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| AbstractList | Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty (USU) that limit the accuracy to which these physical quantities can be determined. The objective of our work has been to develop qualitative and quantitative procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU. It then describes numerical procedures we have introduced to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Several realistic examples are described here to illustrate these procedures and demonstrate their outcomes and limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community. Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty (USU) that limit the accuracy to which these physical quantities can be determined. The objective of our work has been to develop qualitative and quantitative procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU. It then describes numerical procedures we have introduced to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Several realistic examples are described here to illustrate these procedures and demonstrate their outcomes and limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community. Evaluated nuclear data uncertainties reported in the literature or archived in data libraries are often perceived as unrealistic, most often because they are thought to be too small. The impact of this issue in applied nuclear science has been discussed widely in recent years. Commonly suggested causes are: poor estimates of specific error components, neglect of uncertainty correlations, and overlooked known error sources. However, instances have been reported where very careful, objective assessments of all known error sources have been made with realistic error magnitudes and correlations provided, yet the resulting evaluated uncertainties still appear to be inconsistent with observed scatter of predicted mean values. These discrepancies might be attributed to significant unrecognized sources of uncertainty (USU) that limit the accuracy to which these physical quantities can be determined. The objective of our work reported herein has been to develop qualitative and quantitative procedures for revealing and including USU estimates in nuclear data evaluations involving experimental input data. This paper identifies several specific clues that can be explored by evaluators in identifying the existence of USU. It then describes numerical procedures we have introduced to generate quantitative estimates of USU magnitudes. Key requirements for these procedures to be viable are that sufficient numbers of data points be available, for statistical reasons, and that additional supporting information about the measurements be provided by the experimenters. Several realistic examples are described here to illustrate these procedures and demonstrate their outcomes and limitations. Our work strongly supports the view that USU is an important issue in nuclear data evaluation, with significant consequences for applications, and that this topic warrants further investigation by the nuclear science community. |
| Author | Pronyaev, V.G. Duran, I. Schnabel, G. Carlson, A.D. Badikov, S. Smith, D.L. Schillebeeckx, P. Neudecker, D. Gunsing, F. Capote, R. Wallner, A. |
| Author_xml | – sequence: 1 givenname: R. surname: Capote fullname: Capote, R. email: r.capotenoy@iaea.org organization: NAPC–Nuclear Data Section, International Atomic Energy Agency, A-1400 Vienna, Austria – sequence: 2 givenname: S. surname: Badikov fullname: Badikov, S. organization: Atomstandard, Rosatom State Corporation, Moscow, Russia – sequence: 3 givenname: A.D. surname: Carlson fullname: Carlson, A.D. organization: National Institute of Standards and Technology, Gaithersburg, MD 20899, USA – sequence: 4 givenname: I. surname: Duran fullname: Duran, I. organization: Universidade de Santiago de Compostella, 15782 Santiago de Compostela, La Coruna, Spain – sequence: 5 givenname: F. surname: Gunsing fullname: Gunsing, F. organization: CEA Irfu, University Paris-Saclay, F-91191 Gif-sur-Yvette, France – sequence: 6 givenname: D. surname: Neudecker fullname: Neudecker, D. organization: Los Alamos National Laboratory, Los Alamos, NM 87545, USA – sequence: 7 givenname: V.G. surname: Pronyaev fullname: Pronyaev, V.G. organization: Contractor, NAPC–Nuclear Data Section, International Atomic Energy Agency, A-1400 Vienna, Austria – sequence: 8 givenname: P. surname: Schillebeeckx fullname: Schillebeeckx, P. organization: Joint Research Centre - Geel, 2440 Geel, Belgium – sequence: 9 givenname: G. surname: Schnabel fullname: Schnabel, G. organization: Uppsala University, Lagerhyddsvagen, 75120 Uppsala, Sweden – sequence: 10 givenname: D.L. surname: Smith fullname: Smith, D.L. organization: Argonne National Laboratory, 1710 Avenida del Mundo, No. 1506, Coronado, CA 92118, USA – sequence: 11 givenname: A. surname: Wallner fullname: Wallner, A. organization: Australian National University, Canberra ACT, Australia |
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| Title | Unrecognized Sources of Uncertainties (USU) in Experimental Nuclear Data |
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