arXiv subjects

Jacob Forbes

Publications and source records attributed to Jacob Forbes.

3 recordsLinked to original sources

arXiv2026

Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation

This work investigates the use of resonance statistics for resonance evaluation to inform spin group assignment and an alternative fitting objective function beyond the commonly used chi-squared statistic. Resonance statistics -informed methods are applied to the automated resonance fitting framework, developed by N. Walton et al. In this automated framework, the utility of resonance statistics is largely unexplored. The new resonance statistics -informed spin group shuffling algorithm reduces spin group frequency bias seen in the base fitting algorithm. Although resonance statistics -informed optimization produces negligible changes in pointwise cross section agreement, it significantly improves consistency with Wigner level-spacing statistics and stabilizes the fitted resonance density in the presence of model imperfections.

physics.comp-ph↗

arXiv2024

Validating Automated Resonance Evaluation with Synthetic Data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process, reducing uncertainty and enhancing reproducibility. This article contributes to developing a framework for automated evaluation techniques testing, emphasizing automated fitting methods that do not require the user to provide any prior information. This approach simplifies the process and reduces the manual effort needed in the initial evaluation stage. It highlights the capability of the framework to validate and optimize subroutines, targeting the performance analysis and optimization of the fitting procedure using high-fidelity synthetic data (labeled experimental data) and the concept of a fully controlled computational experiment. An error metric is introduced to provide a clear and intuitive measure of the fitting quality by quantifying the accuracy and performance across the specified energy. This metric sets a scale for comparison and optimization of routines or hyperparameter selection, improving the entire evaluation process methodology and increasing reproducibility and objectivity.

physics.comp-ph↗

arXiv2024

Automated Resonance Identification in Nuclear Data Evaluation

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.

physics.comp-ph↗