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Nicolas Bock

Publications and source records attributed to Nicolas Bock.

20 records · Page 2Linked to original sources

Optical Conductivity in a Two-Band Superconductor: Pb

We demonstrate the effect of bandstructure on the superconducting properties of Pb by calculating the strong-coupling features in the optical conductivity, $σ(ω)$, due to the electron-phonon interaction. The importance of momentum dependence in the calculation of the properties of superconductors has previously been raised for MgB$_2$. Pb resembles MgB$_2$ in that it is a two band superconductor in which the bands' contributions to the Fermi surface have very different topologies. We calculate $σ(ω)$ by calculating a memory function which has been recently used to analyze $σ(ω)$ of Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$. In our calculations the two components of the Fermi surface are described by parameterizations of de Haas--van Alphen data. We use a phonon spectrum which is a fit to neutron scattering data. By including the momentum dependence of the Fermi surface good agreement is found with the experimentally determined strong-coupling features which can be described by a broad peak at around 4.5 meV and a narrower higher peak around 8 meV of equal height. The calculated features are found to be dominated by scattering between states within the third band. By contrast scattering between states in the second band leads to strong-coupling features in which the height of the high energy peak is reduced by $\sim 50%$ compared to that of the low energy peak. This result is similar to that in the conventional isotropic (momentum independent) treatment of superconductivity. Our results show that it is important to use realistic models of the bandstructure and phonons, and to avoid using momentum averaged quantities, in calculations in order to get quantitatively accurate results.

cond-mat.supr-con↗

Density Analysis of Network Community Divisions

We present a compact matrix formulation of the modularity, a commonly used quality measure for the community division in a network. Using this formulation we calculate the density of modularities, a statistical measure of the probability of finding a particular modularity for a random but valid community division into $C$ communities. We present our results for some well--known and some artificial networks, and we conclude that the general features of the modularity density are quite similar for the different networks. From a simple model of the modularity we conclude that all nnected networks must show similar shapes of their modularity densities. The general features of this density may give valuable information in the search for good optimization schemes of the modularity.

cond-mat.stat-mech↗