Relativistic nucleon optical potentials with isospin dependence in Dirac Brueckner Hartree-Fock approach

The relativistic optical model potential (OMP) for nucleon-nucleus scattering is investigated in the framework of Dirac-Brueckner-Hartree-Fock (DBHF) approach using the Bonn-B One-Boson- Exchange potential for the bare nucleon-nucleon interaction. Both real and imaginary parts of isospin-dependent nucleon self-energies in nuclear medium are derived from the DBHF approach based on the projection techniques within the subtracted T -matrix representation. The Dirac potentials as well as the corresponding Schrodinger equivalent potentials are evaluated. An improved local density approximation is employed in this analysis, where a range parameter is included to account for a finite-range correction of the nucleon-nucleon interaction. As an example the total cross sections, differential elastic scattering cross sections, analyzing powers for n, p + 27Al at incident energy 100 keV < E < 250 MeV are calculated. The results derived from this microscopic approach of the OMP are compared to the experimental data, as well as the results obtained with a phenomenological OMP. A good agreement between the theoretical results and the measurements can be achieved for all incident energies using a constant value for the range parameter.Comment: 10 pages, 16 figure

Dirac-Brueckner Hartree-Fock Approach: from Infinite Matter to Effective Lagrangians for Finite Systems

One of the open problems in nuclear structure is how to predict properties of finite nuclei from the knowledge of a bare nucleon-nucleon interaction of the meson-exchange type. We point out that a promising starting point consists in Dirac-Brueckner-Hartree-Fock (DBHF) calculations us- ing realistic nucleon-nucleon interactions like the Bonn potentials, which are able to reproduce satisfactorily the properties of symmetric nuclear matter without the need for 3-body forces, as is necessary in non-relativistic BHF calculations. However, the DBHF formalism is still too com- plicated to be used directly for finite nuclei. We argue that a possible route is to define effective Lagrangians with density-dependent nucleon-meson coupling vertices, which can be used in the Relativistic Hartree (or Relativistic Mean Field (RMF)) or preferrably in the Relativistic Hartree- Fock (RHF) approach. The density-dependence is matched to the nuclear matter DBHF results. We review the present status of nuclear matter DBHF calculations and discuss the various schemes to construct the self-energy, which lead to differences in the predictions. We also discuss how effective Lagrangians have been constructed and are used in actual calculations. We point out that completely consistent calculations in this scheme still have to be performed.Comment: 16 pages, to be published in Journal of Physics G: Nuclear and Particle Physics, special issue

Nuclear matter incompressibility coefficient in relativistic and nonrelativistic microscopic models

We systematically analyze the recent claim that nonrelativistic and relativistic mean field (RMF) based random phase approximation (RPA) calculations for the centroid energy E_0 of the isoscalar giant monopole resonance yield for the nuclear matter incompressibility coefficient, K_{nm}, values which differ by about 20%. For an appropriate comparison with the RMF based RPA calculations, we obtain the parameters for the Skyrme force used in the nonrelativistic model by adopting the same procedure as employed in the determination of the NL3 parameter set of an effective Lagrangian used in the RMF model. Our investigation suggest that the discrepancy between the values of K_{nm} predicted by the relativistic and nonrelativistic models is significantly less than 20%.Comment: Revtex file (13 pages), appearing in PRC-Rapid Com

Self-consistent description of nuclear compressional modes

Isoscalar monopole and dipole compressional modes are computed for a variety of closed-shell nuclei in a relativistic random-phase approximation to three different parametrizations of the Walecka model with scalar self-interactions. Particular emphasis is placed on the role of self-consistency which by itself, and with little else, guarantees the decoupling of the spurious isoscalar-dipole strength from the physical response and the conservation of the vector current. A powerful new relation is introduced to quantify the violation of the vector current in terms of various ground-state form-factors. For the isoscalar-dipole mode two distinct regions are clearly identified: (i) a high-energy component that is sensitive to the size of the nucleus and scales with the compressibility of the model and (ii) a low-energy component that is insensitivity to the nuclear compressibility. A fairly good description of both compressional modes is obtained by using a ``soft'' parametrization having a compression modulus of K=224 MeV.Comment: 28 pages and 10 figures; submitted to PR

Surface Incompressibility from Semiclassical Relativistic Mean Field Calculations

By using the scaling method and the Thomas-Fermi and Extended Thomas-Fermi approaches to Relativistic Mean Field Theory the surface contribution to the leptodermous expansion of the finite nuclei incompressibility has been self-consistently computed. The validity of the simplest expansion, which contains volume, volume-symmetry, surface and Coulomb terms, is examined by comparing it with self-consistent results of the finite nuclei incompressibility for some currently used non-linear sigma-omega parameter sets. A numerical estimate of higher-order contributions to the leptodermous expansion, namely the curvature and surface-symmetry terms, is made.Comment: 18 pages, REVTeX, 3 eps figures, changed conten

Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering

As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL