33,982 research outputs found

    Heavy Quark diffusion from lattice QCD spectral functions

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    We analyze the low frequency part of charmonium spectral functions on large lattices close to the continuum limit in the temperature region 1.5≲T/Tc≲31.5\lesssim T/T_c\lesssim 3 as well as for T≃0.75TcT \simeq 0.75T_c. We present evidence for the existence of a transport peak above TcT_c and its absence below TcT_c. The heavy quark diffusion constant is then estimated using the Kubo formula. As part of the calculation we also determine the temperature dependence of the signature for the charmonium bound state in the spectral function and discuss the fate of charmonium states in the hot medium.Comment: 4 pages, Proceedings for Quark Matter 2011 Conference, May 23-28, 2011, Annecy, Franc

    Superconducting gap symmetry of Ba0.6K0.4Fe2As2 studied by angle-resolved photoemission spectroscopy

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    We have performed high-resolution angle-resolved photoemission spectroscopy on the optimally-doped Ba0.6_{0.6}K0.4_{0.4}Fe2_2As2_2 compound and determined the accurate momentum dependence of the superconducting (SC) gap in four Fermi-surface sheets including a newly discovered outer electron pocket at the M point. The SC gap on this pocket is nearly isotropic and its magnitude is comparable (Δ\Delta ∼\sim 11 meV) to that of the inner electron and hole pockets (∼\sim12 meV), although it is substantially larger than that of the outer hole pocket (∼\sim6 meV). The Fermi-surface dependence of the SC gap value is basically consistent with Δ\Delta(kk) = Δ\Delta0_0coskxk_xcoskyk_y formula expected for the extended s-wave symmetry. The observed finite deviation from the simple formula suggests the importance of multi-orbital effects.Comment: 4 pages, 3 figures, 1 tabl

    Charge and spin Hall effect in graphene with magnetic impurities

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    We point out the existence of finite charge and spin Hall conductivities of graphene in the presence of a spin orbit interaction (SOI) and localized magnetic impurities. The SOI in graphene results in different transverse forces on the two spin channels yielding the spin Hall current. The magnetic scatterers act as spin-dependent barriers, and in combination with the SOI effect lead to a charge imbalance at the boundaries. As indicated here, the charge and spin Hall effects should be observable in graphene by changing the chemical potential close to the gap.Comment: 7 page

    A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

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    A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions
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