Abstract
Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross-section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. 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, nonconvex, nonlinear optimization methods are combined with concepts of inferential statistics to infer a resonance model 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 the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.
| Original language | English |
|---|---|
| Pages (from-to) | 1091-1106 |
| Number of pages | 16 |
| Journal | Nuclear Science and Engineering |
| Volume | 199 |
| Issue number | 7 |
| DOIs | |
| State | Published - 2025 |
Funding
This material is based upon work supported by the U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) through the Nuclear Science and Security Consortium under award number [DE-NA0003996]. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, under contract number [DE-AC05-00OR22725] for the DOE. This work was supported by the Nuclear Criticality Safety Program, funded and managed by NNSA for DOE. Work at BNL was sponsored by the Office of Nuclear Physics, Office of Science of DOE under contract number DE-SC0012704 with Brookhaven Science Associates, LLC. This project was supported in part by the BNL, National Nuclear Data Center under the BNL Supplemental Undergraduate Research Program, and by the DOE, Office of Science, Office of Workforce Development for Teachers and Scientists under the Science Undergraduate Laboratory Internships Program. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The authors also gratefully acknowledge Vitaly Ganusov (University of Tennessee) for valuable discussions and insightful feedback during the course of this research regarding model selection approaches.
Keywords
- Resonance
- automation
- evaluation
- reproducibility