Detection of Spiral photons in Quantum Optics

We show that a new type of photon detector, sensitive to the gradients of electromagnetic fields, should be a useful tool to characterize the quantum properties of spatially-dependent optical fields. As a simple detector of such a kind, we propose using magnetic dipole or electric quadrupole transitions in atoms or molecules and apply it to the detection of spiral photons in Laguerre-Gauss (LG) beams. We show that LG beams are not true hollow beams, due to the presence of magnetic fields and gradients of electric fields on beam axis. This approach paves the way to an analysis at the quantum level of the spatial structure and angular momentum properties of singular light beams.Comment: 5 pages, 4 figure

The role of the Berry Phase in Dynamical Jahn-Teller Systems

The presence/absence of a Berry phase depends on the topology of the manifold of dynamical Jahn-Teller potential minima. We describe in detail the relation between these topological properties and the way the lowest two adiabatic potential surfaces get locally degenerate. We illustrate our arguments through spherical generalizations of the linear T x h and H x h cases, relevant for the physics of fullerene ions. Our analysis allows us to classify all the spherical Jahn-Teller systems with respect to the Berry phase. Its absence can, but does not necessarily, lead to a nondegenerate ground state.Comment: revtex 7 pages, 2 eps figures include

The entropy of a correlated system of nucleons

Realistic nucleon-nucleon interaction induce correlations to the nuclear many-body system which lead to a fragmentation of the single-particle strength over a wide range of energies and momenta. We address the question of how this fragmentation affects the thermodynamical properties of nuclear matter. In particular, we show that the entropy can be computed with the help of a spectral function which can be evaluated in terms of the self-energy obtained in the Self-Consistent Green's Function approach. Results for the density and temperature dependences of the entropy per particle for symmetric nuclear matter are presented and compared to the results of lowest order finite temperature Brueckner--Hartree--Fock calculations. The effects of correlations on the calculated entropy are small, if the appropriate quasi-particle approximation is used. The results demonstrate the thermodynamical consistency of the self-consistent T-matrix approximation for the evaluation of the Green's functions.Comment: REVTEX4 - 43 pages, 10 figures - Published versio

The entropy of a correlated system of nucleons

Realistic nucleon-nucleon interaction induce correlations to the nuclear many-body system which lead to a fragmentation of the single-particle strength over a wide range of energies and momenta. We address the question of how this fragmentation affects the thermodynamical properties of nuclear matter. In particular, we show that the entropy can be computed with the help of a spectral function which can be evaluated in terms of the self-energy obtained in the Self-Consistent Green's Function approach. Results for the density and temperature dependences of the entropy per particle for symmetric nuclear matter are presented and compared to the results of lowest order finite temperature Brueckner--Hartree--Fock calculations. The effects of correlations on the calculated entropy are small, if the appropriate quasi-particle approximation is used. The results demonstrate the thermodynamical consistency of the self-consistent T-matrix approximation for the evaluation of the Green's functions.Comment: REVTEX4 - 43 pages, 10 figures - Published versio

Finding instabilities in the community structure of complex networks

The problem of finding clusters in complex networks has been extensively studied by mathematicians, computer scientists and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters, without overlap. We here introduce a method to identify the nodes lying ``between clusters'' and that allows for a general measure of the stability of the clusters. This is done by adding noise over the weights of the edges of the network. Our method can in principle be applied with any clustering algorithm, provided that it works on weighted networks. We present several applications on real-world networks using the Markov Clustering Algorithm (MCL).Comment: 4 pages, 5 figure