An elliptic expansion of the potential field source surface model

Context. The potential field source surface model is frequently used as a basis for further scientific investigations where a comprehensive coronal magnetic field is of importance. Its parameters, especially the position and shape of the source surface, are crucial for the interpretation of the state of the interplanetary medium. Improvements have been suggested that introduce one or more additional free parameters to the model, for example, the current sheet source surface (CSSS) model. Aims. Relaxing the spherical constraint of the source surface and allowing it to be elliptical gives modelers the option of deforming it to more accurately match the physical environment of the specific period or location to be analyzed. Methods. A numerical solver is presented that solves Laplace's equation on a three-dimensional grid using finite differences. The solver is capable of working on structured spherical grids that can be deformed to create elliptical source surfaces. Results. The configurations of the coronal magnetic field are presented using this new solver. Three-dimensional renderings are complemented by Carrington-like synoptic maps of the magnetic configuration at different heights in the solar corona. Differences in the magnetic configuration computed by the spherical and elliptical models are illustrated.Comment: 11 pages, 7 figure

Finite-temperature charge transport in the one-dimensional Hubbard model

We study the charge conductivity of the one-dimensional repulsive Hubbard model at finite temperature using the method of dynamical quantum typicality, focusing at half filling. This numerical approach allows us to obtain current autocorrelation functions from systems with as many as 18 sites, way beyond the range of standard exact diagonalization. Our data clearly suggest that the charge Drude weight vanishes with a power law as a function of system size. The low-frequency dependence of the conductivity is consistent with a finite dc value and thus with diffusion, despite large finite-size effects. Furthermore, we consider the mass-imbalanced Hubbard model for which the charge Drude weight decays exponentially with system size, as expected for a non-integrable model. We analyze the conductivity and diffusion constant as a function of the mass imbalance and we observe that the conductivity of the lighter component decreases exponentially fast with the mass-imbalance ratio. While in the extreme limit of immobile heavy particles, the Falicov-Kimball model, there is an effective Anderson-localization mechanism leading to a vanishing conductivity of the lighter species, we resolve finite conductivities for an inverse mass ratio of $\eta \gtrsim 0.25$.Comment: 13 pages, 11 figure

Comparative study of theoretical methods for nonequilibrium quantum transport

We present a detailed comparison of three different methods designed to tackle nonequilibrium quantum transport, namely the functional renormalization group (fRG), the time-dependent density matrix renormalization group (tDMRG), and the iterative summation of real-time path integrals (ISPI). For the nonequilibrium single-impurity Anderson model (including a Zeeman term at the impurity site), we demonstrate that the three methods are in quantitative agreement over a wide range of parameters at the particle-hole symmetric point as well as in the mixed-valence regime. We further compare these techniques with two quantum Monte Carlo approaches and the time-dependent numerical renormalization group method.Comment: 19 pages, 7 figures; published versio

Lower bounds for the conductivities of correlated quantum systems

We show how one can obtain a lower bound for the electrical, spin or heat conductivity of correlated quantum systems described by Hamiltonians of the form H = H0 + g H1. Here H0 is an interacting Hamiltonian characterized by conservation laws which lead to an infinite conductivity for g=0. The small perturbation g H1, however, renders the conductivity finite at finite temperatures. For example, H0 could be a continuum field theory, where momentum is conserved, or an integrable one-dimensional model while H1 might describe the effects of weak disorder. In the limit g to 0, we derive lower bounds for the relevant conductivities and show how they can be improved systematically using the memory matrix formalism. Furthermore, we discuss various applications and investigate under what conditions our lower bound may become exact.Comment: Title changed; 9 pages, 2 figure

Electronic structure calculations for PrFe4P12 filled skutterudite using Extended Huckel tight-binding method

To get insight into the electronic properties of PrFe4P12 skutterudite, band electronic structure calculations, Total and Projected Density of States, Crystal Orbital Overlap Population and Mulliken Population Analysis were performed. The energy bands yield a semi metallic behavior with a direct gap (at gamma) of 0.02 eV. Total and Projected Density of States provided information of the contribution from each orbital of each atom to the total Density of States. Moreover, the bonding strength between some atoms within the unit cell was obtained. Mulliken Population analysis suggests ionic behavior for this compound

Using Multilevel Outcomes to Construct and Select Biomarker Combinations for Single-level Prediction

Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose an algorithm that leverages the multilevel outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where the kidney injury may be absent, mild, or severe. Using more sophisticated modeling approaches to construct combinations provided gains over the binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. Methods that utilize the multilevel nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations