New approach for the quantification of uncertainties in reaction modeling via data-driven multi-objective optimization

TL;DR

This paper presents a novel data-driven multi-objective optimization method to quantify uncertainties in nuclear reaction modeling, capturing parameter correlations and providing uncertainty estimates across multiple reaction channels.

Contribution

It introduces a new multi-objective optimization approach that accounts for all available data simultaneously to quantify uncertainties in nuclear reaction parameters within the Hauser-Feshbach framework.

Findings

Uncertainty-quantified model parameters for Ni-Ge isotopes.

Estimated resonance spacings for nuclei beyond experimental reach.

Validated method by calculating a known cross-section outside the optimization region.

Abstract

We introduce a new multi-objective optimization approach to determine uncertainty-quantified nuclear reaction parameters in the Hauser-Feshbach framework. By simultaneously accounting for all available data across multiple reaction channels we capture parameter correlations and estimate data-driven uncertainties. We implement in the Ni-Ge region yielding uncertainty-quantified model parameters for both stable and unstable isotopes. We estimate resonance spacings for nuclei beyond experimental reach and validate our method by calculating a known cross-section outside our optimization region.