Bayesian Tools for a Better Optical Model

Optical potentials are pervasive in the description of nuclear reactions and effectively take into account the many-body complexity of the projectile-target system. They are often determined phenomenologically, predominately through fits to elastic scattering data. The parametrizations of these potentials are constrained using reaction data on stable targets but then extrapolated to significantly more exotic (e.g. neutron-rich) systems. The fitting procedure and extrapolation can lead to significant uncertainties in the resulting reaction observables that were typically only quantified by calculating the same theory using two different parametrizations. Recently, we have developed a Bayesian optimization procedure that has been shown to give more realistic uncertainties, compared to standard chi-squared minimization and covariance propagation. Modern statistical tools additionally provide the ability to compare the information content of observables and provide the means to explore which experiments would be most useful for giving insights and constraining theoretical models. In this talk, we discuss three such tools: principal component analysis, sensitivity analysis, and Bayesian evidence. We first apply these tools to a toy model to demonstrate their effectiveness and then use them to investigate the information content of two reaction observables.

LA-UR-22-25990