From noise to information: The transfer function formalism for uncertainty quantification on nuclear density reconstruction

TL;DR

This paper introduces the transfer function formalism for uncertainty quantification in neutron density reconstruction, enabling optimized experimental design and model selection for nuclear physics measurements.

Contribution

The transfer function formalism provides an analytical framework for quantifying uncertainties and optimizing experiments in neutron density measurements, a novel approach in nuclear density analysis.

Findings

Analyzed the expected uncertainty in neutron density measurements.

Identified optimal models and experimental locations for minimal uncertainty.

Explored the influence of prior distributions and hyperparameters on results.

Abstract

The neutron distribution of neutron-rich nuclei provides critical information on the structure of finite nuclei and neutron stars. Parity violating experiments -- such as PREX and CREX -- provide a clean and largely model-independent determination of neutron densities. Such experiments, however, are challenging and expensive which is why sound statistical arguments are required to maximize the information gained. For this goal we introduce a new framework, "the transfer function formalism", aimed at uncertainty quantification, model selection, and experimental design in the context of neutron densities. The transfer functions (TFs) are built analytically by expressing the linear response of the objective function to small perturbations of the data. Using the TF formalism, we are able to analyze the expected overall uncertainty -- quantified in terms of bias and variance -- of the mean square radius and interior density of $^{48}$Ca and $^{208}$Pb. Using relativistic mean field models as a proxy for the weak-charge density -- and assuming that a total of five measurements could be performed on the weak form factor of $^{48}$Ca and $^{208}$Pb -- we identify the optimal models and experimental locations that minimize the combined radius and interior uncertainty for both nuclei. We also explore the use of the TF formalism to understand the influence of prior distributions for the model parameters, as well as the optimization of model hyperparameters not constrained by the data.