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Related papers: Separable RPA for self-consistent nuclear models

200 papers

The self-consistent random phase approximation (RPA) based on a correlated realistic nucleon-nucleon interaction is used to evaluate correlation energies in closed-shell nuclei beyond the Hartree-Fock level. The relevance of contributions…

Nuclear Theory · Physics 2007-05-23 C. Barbieri , N. Paar , R. Roth , P. Papakonstantinou

We extend the capabilities of correlation energy functionals based on the adiabatic-connection fluctuation-dissipation theorem by implementing the analytical atomic forces within the random phase approximation (RPA), in the context of plane…

Materials Science · Physics 2026-03-19 Damian Contant , Maria Hellgren

We make use of a subtraction procedure, introduced to overcome double--counting problems in beyond--mean--field theories, in the second random--phase--approximation (SRPA) for the first time. This procedure guarantees the stability of SRPA…

Nuclear Theory · Physics 2015-09-09 D. Gambacurta , M. Grasso , J. Engel

We have developed a fully consistent framework for calculations in the Quasiparticle Random Phase Approximation (QRPA) with $NN$ interactions from the Similarity Renormalization Group (SRG) and other unitary transformations of realistic…

Nuclear Theory · Physics 2011-07-04 H. Hergert , P. Papakonstantinou , R. Roth

Developing theoretical understanding of complex reactions and processes at interfaces requires using methods that go beyond semilocal density functional theory to accurately describe the interactions between solvent, reactants and…

We investigate the applicability of finite temperature random phase approximation (RPA) using a solvable Lipkin model. We show that the finite temperature RPA reproduces reasonably well the temperature dependence of total strength, both for…

Nuclear Theory · Physics 2009-11-06 K. Hagino , F. Minato

We consider several spin-unrestricted random-phase approximation (RPA) variants for calculating correlation energies, with and without range separation, and test them on datasets of atomization energies and reaction barrier heights. We show…

Chemical Physics · Physics 2016-03-01 Bastien Mussard , Peter Reinhardt , Janos Angyan , Julien Toulouse

The framework of the Perturbed Static Path Approximation (PSPA) is used to calculate the partition function of a finite Fermi system from a Hamiltonian with a separable two body interaction. Therein, the collective degree of freedom is…

Condensed Matter · Physics 2009-11-07 C. Rummel , H. Hofmann

A self-consistent random phase approximation (RPA) is proposed as an effective Hamiltonian method in Light-Front Field Theory (LFFT). We apply the general idea to the light-front massive Schwinger model to obtain a new bound state equation…

High Energy Physics - Theory · Physics 2009-10-31 Koji Harada

We present a new method to obtain interaction part of a model Hamiltonian from the result of the first-principles calculation. The effective interaction contained in the model is determined based on the random phase approximation (RPA). In…

Materials Science · Physics 2017-04-26 Hirofumi Sakakibara , Seung Woo Jang , Hiori Kino , Myung Joon Han , Kazuhiko Kuroki , Takao Kotani

An exactly solvable model is introduced, which is equivalent to the exact shell-model treatment of protons and neutrons in a single j-shell for Fermi-type excitations. Exact energies, quasiparticle numbers and double beta decay Fermi…

Nuclear Theory · Physics 2007-05-23 Jorge G. Hirsch , Peter O. Hess , Osvaldo Civitarese

Several approaches to photonuclear reactions, based on the time-dependent density-functional theory, have been developed recently. The standard linearization leads to the random-phase approximation (RPA) or the quasiparticle-random-phase…

Self-consistent relativistic random-phase approximation (RPA) in the radial coordinate representation is established by using the finite amplitude method (FAM). Taking the isoscalar giant monopole resonance in spherical nuclei as example,…

Nuclear Theory · Physics 2013-10-16 Haozhao Liang , Takashi Nakatsukasa , Zhongming Niu , Jie Meng

Density functional theory is a preferred microscopic method for calculation of nuclear properties over the whole nuclear chart. Besides ground-state properties, which are calculated by Hartree-Fock theory, nuclear excitations can be…

Nuclear Theory · Physics 2020-06-02 Anton Repko

The Green's function method in the \emph{Quasiparticle Time Blocking Approximation} is applied to nuclear excitations in $^{132}$Sn and $^{208}$Pb. The calculations are performed self-consistently using a Skyrme interaction. The method…

Nuclear Theory · Physics 2008-11-26 N. Lyutorovich , J. Speth , A. Avdeenkov , F. Gruemmer , S. Kamerdzhiev , S. Krewald , V. I. Tselyaev

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of…

Materials Science · Physics 2017-07-26 Xinguo Ren , Patrick Rinke , Christian Joas , Matthias Scheffler

We propose a high-speed and accurate hybrid dynamic density functional theory for the computer simulations of the phase separation processes of polymer melts and blends. The proposed theory is a combination of the dynamic self-consistent…

Soft Condensed Matter · Physics 2009-11-11 Takashi Honda , Toshihiro Kawakatsu

In this paper we develop a theoretical framework which allows us to study excitations of the nucleon. Assuming an effective two-body interaction as a model for low-energy QCD, we derive a relativistic TDHF equation for a many-body system of…

High Energy Physics - Phenomenology · Physics 2016-09-01 S. Hardt , J. Geiss , H. Lenske , U. Mosel

The problems related to the existence of the spurious dipole mode (SDM) in the self-consistent nuclear-structure models are considered. A method is formulated that allows to eliminate coupling of the SDM with the physical modes in the…

Nuclear Theory · Physics 2023-01-04 V. Tselyaev

Principal Component Analysis (PCA) has been widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA), under different robust distance metrics, such as l1-norm and l2, p-norm, can deal with noise or outliers to some…

Machine Learning · Computer Science 2021-06-29 Zhao Kang , Hongfei Liu , Jiangxin Li , Xiaofeng Zhu , Ling Tian