Nuclear density functional theory (DFT) and ab initio theory are complementary approaches to nuclear structure. DFT can be applied to the whole nuclear chart, but relies on empirical energy density functionals (EDFs). As a consequence, predictions in regions where experimental data are lacking, e.g. in neutron-rich nuclei close to the driplines, are subject to significant uncertainties. In contrast, ab initio theory allows to draw predictions with controlled uncertainties, but there are difficulties in extending these methods to heavy nuclei or e.g. far from magic numbers. The purpose of this thesis is to combine DFT and ab initio by grounding the nuclear EDFs into ab initio theoretical predictions, specifically on infinite nuclear matter calculations. The ab initio Quantum Monte Carlo (QMC) and Self-consistent Green’s functions (SCGF) methods are employed to study nuclear matter. An extension of SCGF based on the algebraic diagrammatic construction (ADC-SCGF) is developed to include Gorkov pairing correlations. The ab initio equations of state (EOS) are used to construct EDFs based on the local density approximation and gradient approximation, which are applied to magic nuclei. Then, the static response of nuclear matter is tackled with both DFT and QMC, and a study of the constraints that can be set on the EDF surface terms from ab initio is reported. This thesis outlines a new strategy to ground nuclear EDFs into ab initio and implements some of its pillars. A critical discussion of the difficulties encountered is given. Future developments include the study of nuclear matter from a microscopic perspective, e.g. extending ADC-SCGF to tackle the dynamic response and superfluid matter.

MICROSCOPIC THEORY OF INFINITE NUCLEAR MATTER AND NON-EMPIRICAL ENERGY FUNCTIONALS / F. Marino ; supervisor: G. Colò ; cosupervisor: F. Pederiva : coordinatore: R. Vecchi. Dipartimento di Fisica Aldo Pontremoli, 2023. 36. ciclo, Anno Accademico 2023.

MICROSCOPIC THEORY OF INFINITE NUCLEAR MATTER AND NON-EMPIRICAL ENERGY FUNCTIONALS

F. Marino
2023

Abstract

Nuclear density functional theory (DFT) and ab initio theory are complementary approaches to nuclear structure. DFT can be applied to the whole nuclear chart, but relies on empirical energy density functionals (EDFs). As a consequence, predictions in regions where experimental data are lacking, e.g. in neutron-rich nuclei close to the driplines, are subject to significant uncertainties. In contrast, ab initio theory allows to draw predictions with controlled uncertainties, but there are difficulties in extending these methods to heavy nuclei or e.g. far from magic numbers. The purpose of this thesis is to combine DFT and ab initio by grounding the nuclear EDFs into ab initio theoretical predictions, specifically on infinite nuclear matter calculations. The ab initio Quantum Monte Carlo (QMC) and Self-consistent Green’s functions (SCGF) methods are employed to study nuclear matter. An extension of SCGF based on the algebraic diagrammatic construction (ADC-SCGF) is developed to include Gorkov pairing correlations. The ab initio equations of state (EOS) are used to construct EDFs based on the local density approximation and gradient approximation, which are applied to magic nuclei. Then, the static response of nuclear matter is tackled with both DFT and QMC, and a study of the constraints that can be set on the EDF surface terms from ab initio is reported. This thesis outlines a new strategy to ground nuclear EDFs into ab initio and implements some of its pillars. A critical discussion of the difficulties encountered is given. Future developments include the study of nuclear matter from a microscopic perspective, e.g. extending ADC-SCGF to tackle the dynamic response and superfluid matter.
19-dic-2023
COLO', GIANLUCA
VECCHI, ROBERTA
Doctoral Thesis
MICROSCOPIC THEORY OF INFINITE NUCLEAR MATTER AND NON-EMPIRICAL ENERGY FUNCTIONALS / F. Marino ; supervisor: G. Colò ; cosupervisor: F. Pederiva : coordinatore: R. Vecchi. Dipartimento di Fisica Aldo Pontremoli, 2023. 36. ciclo, Anno Accademico 2023.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1023132
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