Random-phase-approximation-based correlation energy functionals: Benchmark results for atoms

The random phase approximation (RPA) for the correlation energy functional of density functional theory has recently attracted renewed interest. Formulated in terms of the Kohn-Sham (KS) orbitals and eigenvalues, it promises to resolve some of the fundamental limitations of the local density and generalized gradient approximations, as for instance their inability to account for dispersion forces. First results for atoms, however, indicate that the RPA overestimates correlation effects as much as the orbital-dependent functional obtained by a second order perturbation expansion on the basis of the KS Hamiltonian. In this contribution, three simple extensions of the RPA are examined, (a) its augmentation by an LDA for short-range correlation, (b) its combination with the second order exchange term, and (c) its combination with a partial resummation of the perturbation series including the second order exchange. It is found that the ground state and correlation energies as well as the ionization potentials resulting from the extensions (a) and (c) for closed sub-shell atoms are clearly superior to those obtained with the unmodified RPA. Quite some effort is made to ensure highly converged RPA data, so that the results may serve as benchmark data. The numerical techniques developed in this context, in particular for the inherent frequency integration, should also be useful for applications of RPA-type functionals to more complex systems.Comment: 11 pages, 7 figure

Dispersion Laws for In-medium Fermions and Gluons in the CFL Phase of QCD

We evaluate several quantities appearing in the effective lagrangian for the color-flavor locked phase of high density QCD using a formalism which exploits the approximate decoupling of fermions with energy negative with respect to the Fermi energy. The effective theory is essentially two-dimensional and exhibits a Fermi velocity superselection rule, similar to the one found in the Heavy Quark Effective Theory. Within the formalism we reproduce, using gradient expansion, the results for the effective parameters of the Nambu-Goldstone bosons. We also determine the dispersion laws for the gluons. By coupling the theory to fermions and integrating over the two-dimensional degrees of freedom we obtain the effective description of in-medium fermions.Comment: 17 pages, LaTex, 2 figures. Version published in Phys. Lett. B with an arithmetic misprint corrected in eq. (62) (and as a consequence in eqs. (63), (66) and (73)

Critical comments on the paper "Crossing ω=−1\omega=-1 by a single scalar field on a Dvali-Gabadadze-Porrati brane" by H Zhang and Z-H Zhu [Phys.Rev.D75,023510(2007)]

It is demonstrated that the claim in the paper "Crossing $\omega=-1$ by a single scalar field on a Dvali-Gabadadze-Porrati brane" by H Zhang and Z-H Zhu [Phys.Rev.D75,023510(2007)], about a prove that there do not exist scaling solutions in a universe with dust in a Dvali-Gabadadze-Porrati (DGP) braneworld scenario, is incorrect.Comment: 5 pages, 8 eps figure

Mission Analysis Program for Solar Electric Propulsion (MAPSEP). Volume 1: Analytical manual

The mission analysis program for solar electric propulsion (MAPSEP) is comprised of the basic modes: TOPSEP (trajectory generation), GODSEP (linear error analysis), and SIMSEP (simulation). The program is designed to analyze any low thrust mission with respect to trajectory performance, guidance and navigation, and to provide system related requirements for the purpose of vehicle design. The MAPSEP organization is described along with all models and algorithms. Topics discussed include: trajectory and error covariance propagation methods, orbit determination processes, thrust modeling, and trajectory correction (guidance) schemes

Mission Analysis Program for Solar Electric Propulsion (MAPSEP). Volume 3: Program manual

The internal structure of MAPSEP is described. Topics discussed include: macrologic, variable definition, subroutines, and logical flow. Information is given to facilitate modifications to the models and algorithms of MAPSEP