A Computational Framework for Automated Nuclear Resonance Evaluation and Validation

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. This thesis presents two major, novel methodological contributions to the field of nuclear data evaluation, with a focus on resonance region cross sections. The first is a methodology for automating resonance parameter inference, saving valuable time for evaluators and analysts, while also enhancing reproducibility. The second is a computational framework that leverages high-utility generative modeling to test, validate, and benchmark the performance of inferential methods. The integration of these two approaches enables a quantitative assessment of the automated algorithm’s performance and establishes a framework that can be broadly applied to address a wide range of scientific questions. Several demonstrations highlight the computational experiments made possible by the framework, and the final results of the automated algorithm are compared against human evaluation of actual measurement data for Ta-181.