  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.
