Uncertainty-quantified reaction modeling via data-driven multi-objective optimization.

We have recently introduced a new multi-objective optimization approach that allows us to determine uncertainty-quantified

nuclear reaction parameters in the Hauser-Feshbach framework by simultaneously accounting for a precise theoretical description of

all available experimental data across multiple reaction channels in a given region of the nuclear chart. From this analysis, we capture parameter correlations and estimate

data-driven uncertainties for theoretical parameters of optical potentials and of level densities. We extract uncertainty-quantified resonance spacing values for all nuclei in the optimization network and thus propose estimated values for both stable and unstable isotopes. In this talk, we present the implementation of our approach in the Ni-Ge region and preliminary results of the same analysis for nuclei around ⁹⁶Zr.