Symmetry energy in nuclear density functional theory

Nazarewicz, W.; Reinhard, P.‐G.; Satuła, W.; Vretenar, Dario

doi:10.1140/epja/i2014-14020-3

Cited by 53 publications

(71 citation statements)

References 99 publications

(185 reference statements)

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“…However, other correlations shown in the literature with E sym or K sym [11,47] are not observed here, which might again indicate the model dependence of these correlations.…”

Section: B Correlationssupporting

confidence: 50%

“…As a consequence, the different EoS parameters extracted from the data fit exhibit strong correlations [10][11][12][13]. Some of these correlations might reflect physical properties of the EoS.…”

Section: Introductionmentioning

confidence: 99%

“…The proliferation of extra parameters, though essential to pin down the complexity of nuclear structure, makes it hard to sort out the specific effect of EoS parameters on a given nuclear observable [11]. To give an example, this problem is shown by different recent works analyzing the EoS dependence of the neutron skin in 208 Pb.…”

Section: Introductionmentioning

confidence: 99%

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Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach

et al. 2017

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The question of correlations among empirical equation of state (EoS) parameters constrained by nuclear observables is addressed in a fully empirical meta-modeling approach. A recently proposed meta-modeling for the nuclear EoS in nuclear matter, is augmented with a single finite size term to produce a minimal unified EoS functional able to describe the smooth part of the nuclear ground state properties. This meta-model can reproduce the predictions of a large variety of models, and interpolate continuously between them. The parameter space is sampled and filtered through the constraint of nuclear mass reproduction. We show that this simple analytical meta-modeling has a predictive power on masses, radii, and skins comparable to full HF or ETF calculations with realistic energy functionals. The covariance analysis on the posterior distribution shows that no physical correlation is present between the different EoS parameters. Concerning nuclear observables, a strong correlation between the slope of the symmetry energy and the neutron skin is observed, in agreement with previous studies.

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“…However, other correlations shown in the literature with E sym or K sym [11,47] are not observed here, which might again indicate the model dependence of these correlations.…”

Section: B Correlationssupporting

confidence: 50%

Section: Introductionmentioning

confidence: 99%

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Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach

et al. 2017

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“…This allows to consider the NMP also as model parameters. This is a more intuitive way to communicate the model parameters and it allow direct comparison with other mean-field models which are often also fully mappable to NMP, see for example the comparison of symmetry energies in [56]. isoscalar ground state properties equilibrium density:…”

Section: Homogeneous Nuclear Mattermentioning

confidence: 99%

Estimating the relevance of predictions from the Skyrme–Hartree–Fock model

Reinhard

2015

Phys. Scr.

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This contribution reviews the present status of the Skyrme-HartreeFock (SHF) approach as one of the leading self-consistent mean-field models in the physics of atomic nuclei. It starts with a brief summary of the formalism and strategy for proper calibration of the SHF functional. The main emphasis lies on an exploration of the reliability of predictions, particularly in the regime of extrapolations. Various strategies are discussed to explore the statistical and systematic errors of SHF. The strategies are illustrated on examples from actual applications. Variations of model and fit data are used to get an idea about systematic errors. The statistical error is evaluated in straightforward manner by statistical analysis based on χ 2 fits. This also allows also to evaluate the correlations (covariances) between observables which provides useful insights into the structure of the model and of the fitting strategy.

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“…In the meantime, parametrizations have been steadily developed further including more and more data and exploiting the benefits of the χ 2 techniques, for recent examples see, e.g., [7,8]. The availability of these thoroughly fitted parametrizations allowed a series of extensive studies of correlations within the models using covariance analysis which revealed interesting inter-relations between symmetry energy, neutron radius, and dipole spectra [9,10,11,12]. In this paper, we want to discuss error analysis from a more general perspective.…”

Section: Introductionmentioning

confidence: 99%

Error estimates for the Skyrme–Hartree–Fock model

Erler

Reinhard

2015

J. Phys. G: Nucl. Part. Phys.

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Abstract. There are many complementing strategies to estimate the extrapolation errors of a model which was calibrated in least-squares fits. We consider the SkyrmeHartree-Fock model for nuclear structure and dynamics and exemplify the following five strategies: uncertainties from statistical analysis, covariances between observables, trends of residuals, variation of fit data, dedicated variation of model parameters. This gives useful insight into the impact of the key fit data as they are: binding energies, charge r.m.s. radii, and charge formfactor. Amongst others, we check in particular the predictive value for observables in the stable nucleus 208 Pb, the super-heavy element 266 Hs, r-process nuclei, and neutron stars.Error estimates for the Skyrme-Hartree-Fock model 2

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Symmetry energy in nuclear density functional theory

Cited by 53 publications

References 99 publications

Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach

Constraints on the nuclear equation of state from nuclear masses and radii in a Thomas-Fermi meta-modeling approach

Estimating the relevance of predictions from the Skyrme–Hartree–Fock model

Error estimates for the Skyrme–Hartree–Fock model

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