Multireference Covariant Density Functional Theory with Stochastic Basis

TL;DR

This paper introduces a stochastic-basis multireference density functional theory (MR-SDFT) that enhances traditional methods by generating diverse configurations with a stochastic field and selecting relevant ones, improving nuclear property predictions.

Contribution

The novel MR-SDFT scheme combines stochastic configuration generation and projection-based selection to better capture collective correlations in nuclear structure calculations.

Findings

Lower ground-state energies for selected nuclei.

Smaller point-proton rms radii observed.

Softer ground-state band compared to conventional methods.

Abstract

Multireference density functional theory (MR-DFT) provides a pivotal microscopic framework for the description of the ground state properties, low-lying nuclear spectra and transition properties of atomic nuclei. Conventionally, practical implementations of MR-DFT rely on empirically chosen generator coordinates, which may omit relevant collective degrees of freedom and thus fail to capture sufficient collective correlations. Here we introduce the stochastic-basis multireference density functional theory (MR-SDFT). This is an extended scheme that augments the MR-DFT toolkit by (i) generating a diverse ensemble of mean-field reference configurations via a stochastic external field and (ii) selecting a compact subspace with Projection-Selection method. The chosen reference configurations are then linearly superposed within the MR-DFT framework to yield spectroscopic observables. Applying this framework to \nuclide[20]{Ne}, \nuclide[24]{Mg} and \nuclide[28]{Si} with the covariant density functional theory (CDFT), it is demonstrated that the MR-SCDFT leads to lower ground-state energies, smaller point-proton rms radius, and a softer ground-state band compared to the conventional MR-CDFT.