Nuclear Energy Density Optimization

TL;DR

This paper presents a new optimization method for nuclear energy density models that improves accuracy and reliability, resulting in a parameter set that aligns well with experimental data and offers new physics insights.

Contribution

The paper introduces a derivative-free optimization algorithm for nuclear energy density models, enhancing reliability, speed, and accuracy over standard methods.

Findings

Parameter set UNEDFpre matches experimental data well.

The optimization method outperforms standard techniques.

Covariance analysis provides new physics insights.

Abstract

We carry out state-of-the-art optimization of a nuclear energy density of Skyrme type in the framework of the Hartree-Fock-Bogoliubov (HFB) theory. The particle-hole and particle-particle channels are optimized simultaneously, and the experimental data set includes both spherical and deformed nuclei. The new model-based, derivative-free optimization algorithm used in this work has been found to be significantly better than standard optimization methods in terms of reliability, speed, accuracy, and precision. The resulting parameter set UNEDFpre results in good agreement with experimental masses, radii, and deformations and seems to be free of finite-size instabilities. An estimate of the reliability of the obtained parameterization is given, based on standard statistical methods. We discuss new physics insights offered by the advanced covariance analysis.