Statistical tools for a better optical model

TL;DR

This paper evaluates statistical tools like PCA, sensitivity analysis, and Bayesian evidence to improve the modeling of nuclear optical potentials, demonstrating their effectiveness in analyzing experimental data and constraining model parameters.

Contribution

It introduces and applies three statistical tools to nuclear reaction data, providing insights into their relative effectiveness for constraining optical model parameters.

Findings

Bayesian evidence effectively discriminates model components at different energies.

Elastic scattering and polarization data have comparable constraining power.

Angular dependence significantly affects the sensitivity of optical model parameters.

Abstract

Background: Modern statistical tools provide the ability to compare the information content of observables and provide a path to explore which experiments would be most useful to give insight into and constrain theoretical models. Purpose: In this work we study three such tools in the context of nuclear reactions with the goal of constraining the optical potential. Method: The three statistical tools examined are: i) the principal component analysis; ii) the sensitivity analysis based on derivatives; and iii) the Bayesian evidence. We first apply these tools to a toy-model case, comparing the form of the imaginary part of the optical potential. Then we consider two different reaction observables, elastic angular distributions and polarization data for reactions on 48Ca at two different beam energies. Results: For the toy-model case, we find significant discrimination power in the sensitivities and the Bayesian evidence, showing clearly that the volume imaginary term is more useful to describe scattering at higher energies. When comparing between elastic cross sections and polarization data using realistic optical models, sensitivity studies indicate that both observables are roughly equally sensitive but the variability of the optical model parameters is strongly angle dependent. The Bayesian evidence shows some variability between the two observables, but the Bayes factor obtained is not sufficient to discriminate between angular distributions and polarization. Conclusions: From the cases considered, we conclude that in general elastic scattering angular distributions have similar impact in constraining the optical potential parameters compared to the polarization data. The angular ranges for the optimum experimental constraints can vary significantly with the observable considered.