Automated Resonance Identification in Nuclear Data Evaluation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909603168518144 |
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| author | Walton, Noah A. W. Zivenko, Oleksii Fritsch, William Forbes, Jacob Lewis, Amanda Brown, Jesse Sobes, Vlad |
| author_facet | Walton, Noah A. W. Zivenko, Oleksii Fritsch, William Forbes, Jacob Lewis, Amanda Brown, Jesse Sobes, Vlad |
| contents | Global and national efforts to deliver high-quality nuclear data to users have a broad impact across applications such as national security, reactor operation, basic science, medical fields, and more. Cross section evaluation is a large part this effort as it combines theory and experiment to produce suggested values and uncertainty for reaction probabilities. In most isotopes, the cross section exhibits resonant behavior in what is called the resonance region of incident neutron energy. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant, manual effort and months of time from expert scientists. In this article, non-convex, non-linear optimization methods are combined with concepts of inferential statistics to infer a set of optimized resonance models from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and prior biases while improving reproducibility and document-ability, addressing widely recognized challenges in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Resonance Identification in Nuclear Data Evaluation Walton, Noah A. W. Zivenko, Oleksii Fritsch, William Forbes, Jacob Lewis, Amanda Brown, Jesse Sobes, Vlad Computational Physics Nuclear Theory Global and national efforts to deliver high-quality nuclear data to users have a broad impact across applications such as national security, reactor operation, basic science, medical fields, and more. Cross section evaluation is a large part this effort as it combines theory and experiment to produce suggested values and uncertainty for reaction probabilities. In most isotopes, the cross section exhibits resonant behavior in what is called the resonance region of incident neutron energy. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant, manual effort and months of time from expert scientists. In this article, non-convex, non-linear optimization methods are combined with concepts of inferential statistics to infer a set of optimized resonance models from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and prior biases while improving reproducibility and document-ability, addressing widely recognized challenges in the field. |
| title | Automated Resonance Identification in Nuclear Data Evaluation |
| topic | Computational Physics Nuclear Theory |
| url | https://arxiv.org/abs/2402.14122 |