Benchmarking mean-field approximations to level densities
Y. Alhassid, G.F. Bertsch, C.N. Gilbreth, and H. Nakada

TL;DR
This paper evaluates the accuracy of finite-temperature mean-field theories in predicting nuclear level densities by comparing them with shell model Monte Carlo calculations for specific nuclei, highlighting key weaknesses and proposing an improved approximation method.
Contribution
The study systematically assesses mean-field approximations against shell model results and introduces an alternative saddle-point method for better particle-number projection.
Findings
Mean-field theories show specific errors in entropy and level density predictions.
The alternative saddle-point approximation improves particle-number projection accuracy.
HFB theory remains accurate within one entropy unit for certain nuclei above the pairing transition.
Abstract
We assess the accuracy of finite-temperature mean-field theory using as a standard the Hamiltonian and model space of the shell model Monte Carlo calculations. Two examples are considered: the nucleus Dy, representing a heavy deformed nucleus, and Sm, representing a nearby heavy spherical nucleus with strong pairing correlations. The errors inherent in the finite-temperature Hartree-Fock and Hartree-Fock-Bogoliubov approximations are analyzed by comparing the entropies of the grand canonical and canonical ensembles, as well as the level density at the neutron resonance threshold, with shell model Monte Carlo (SMMC) calculations, which are accurate up to well-controlled statistical errors. The main weak points in the mean-field treatments are seen to be: (i) the extraction of number-projected densities from the grand canonical ensembles, and (ii) the symmetry breaking by…
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