Ab initio description of monopole resonances in light- and medium-mass nuclei
Andrea PORRO
Mardi 26/09/2023, 10:00-12:00
Bat 703, p 135 salle visio-conférence, CEA Saclay, Orme des Merisiers


Giant resonances (GRs) are the clearest manifestation of collective motions in the atomic nucleus. They can best be imagined in terms of vibrations of the nuclear surface in a liquid-drop picture, in which most, if not all, the nucleons take part to the process. In particular, the Giant Monopole Resonance (GMR) is also directly linked to the incompressibility of infinite nuclear matter, a key quantity entering the nuclear equation of state.
The GMR has largely been investigated, from a theoretical perspective, via the Quasiparticle Random Phase Approximations (QRPA) in the context of Energy Density Functional theories. In this frame, phenomenological interactions are used to solve the many-body Schrödinger equation under the assumption that excited states can be described as harmonic vibrations on an uncorrelated ground state, possibly breaking the symmetries of the nuclear Hamiltonian.
The last twenty years, in parallel, the so-called ab initio methods became a reliable tool to access nuclear ground-state properties, starting from realistic interactions rooted into the underlying quantum chromodynamics via chiral Effective Field Theory. Ab initio QRPA solvers were also developed, nowadays addressing Giant Resonances (GRs) in (doubly-) open-shell systems. 
In this work, the Projected Generator Coordinate Method (PGCM) is used in an ab initio context for the first time to investigate the GMR in light- and medium-mass nuclei. This method is powerful to overcome some drawbacks, implicit in the formalisation of deformed QRPA; also, it allows treating the presence of anharmonic effects in an exact fashion. The comparison to consistent QRPA calculations is explicitly addressed, establishing ab initio PGCM as a trustful method to investigate the Physics of GRs.


Description des résonances monopolaires pour des noyaux de masse légère et moyenne par des méthodes ab initio.

Résumé dans le fichier PDF.

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