| 1. |
- Salvioni, G., et al.
(author)
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Model nuclear energy density functionals derived from ab initio calculations
- 2020
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In: Journal of Physics G: Nuclear and Particle Physics. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:8
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Journal article (peer-reviewed)abstract
- We present the first application of a new approach, proposed in (2016 J. Phys. G: Nucl. Part. Phys. 43 04LT01) to derive coupling constants of the Skyrme energy density functional (EDF) from ab initio Hamiltonian. By perturbing the ab initio Hamiltonian with several functional generators defining the Skyrme EDF, we create a set of metadata that is then used to constrain the coupling constants of the functional. We use statistical analysis to obtain such an ab initio-equivalent Skyrme EDF. We find that the resulting functional describes properties of atomic nuclei and infinite nuclear matter quite poorly. This may point to the necessity of building up the ab initio-equivalent functionals from more sophisticated generators. However, we also indicate that the current precision of the ab initio calculations may be insufficient for deriving meaningful nuclear EDFs.
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| 2. |
- Idini, A.
(author)
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Statistical learnability of nuclear masses
- 2020
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In: Physical Review Research. - 2643-1564. ; 2:4
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Journal article (peer-reviewed)abstract
- After more than 80 years from the seminal work of Weizsäcker and the liquid drop model of the atomic nucleus, deviations from experiments of mass models (~MeV) are orders of magnitude larger than experimental errors (~keV). Predicting the mass of atomic nuclei with precision is extremely challenging. This is due to the nontrivial many-body interplay of protons and neutrons in nuclei, and the complex nature of the nuclear strong force. Statistical theory of learning will be used to provide the bounds to prediction errors of a model trained with a finite data set. These bounds are validated with neural network models and compared with state of the art mass models. It will be argued that nuclear structure mass models explore a system on the limit of the precision bounds, as defined by the statistical theory of learning.
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| 3. |
- Johnson, Calvin W., et al.
(author)
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White paper: From bound states to the continuum
- 2020
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In: Journal of Physics G: Nuclear and Particle Physics. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 47:12
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Research review (peer-reviewed)abstract
- This white paper reports on the discussions of the 2018 Facility for Rare Isotope Beams Theory Alliance (FRIB-TA) topical program ‘From bound states to the continuum: Connecting bound state calculations with scattering and reaction theory’. One of the biggest and most important frontiers in nuclear theory today is to construct better and stronger bridges between bound state calculations and calculations in the continuum, especially scattering and reaction theory, as well as teasing out the influence of the continuum on states near threshold. This is particularly challenging as many-body structure calculations typically use a bound state basis, while reaction calculations more commonly utilize few-body continuum approaches. The many-body bound state and few-body continuum methods use different language and emphasize different properties. To build better foundations for these bridges, we present an overview of several bound state and continuum methods and, where possible, point to current and possible future connections.
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