Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation

TL;DR

This paper introduces resonance statistics-informed methods to improve automated resonance fitting, enhancing consistency with theoretical level-spacing statistics and stabilizing resonance density estimates despite model imperfections.

Contribution

It presents a novel resonance statistics-informed spin group shuffling algorithm that reduces bias and improves the stability of resonance density estimates in automated fitting.

Findings

Reduces spin group frequency bias in resonance fitting.

Improves consistency with Wigner level-spacing statistics.

Stabilizes resonance density estimates despite model imperfections.

Abstract

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.