1,646 research outputs found

    Interference and interaction effects in multi-level quantum dots

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    Using renormalization group techniques, we study spectral and transport properties of a spinless interacting quantum dot consisting of two levels coupled to metallic reservoirs. For strong Coulomb repulsion UU and an applied Aharonov-Bohm phase ϕ\phi, we find a large direct tunnel splitting Δ(Γ/π)cos(ϕ/2)ln(U/ωc)|\Delta|\sim (\Gamma/\pi)|\cos(\phi/2)|\ln(U/\omega_c) between the levels of the order of the level broadening Γ\Gamma. As a consequence we discover a many-body resonance in the spectral density that can be measured via the absorption power. Furthermore, for ϕ=π\phi=\pi, we show that the system can be tuned into an effective Anderson model with spin-dependent tunneling.Comment: 5 pages, 4 figures included, typos correcte

    Spiral-Induced Star Formation in the Outer Disks of Galaxies

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    The outer regions of galactic disks have received increased attention since ultraviolet observations with GALEX demonstrated that nearly 30% of galaxies have UV emission beyond their optical extents, indicating star formation activity. These galaxies have been termed extended UV (XUV) disks. Here, we address whether these observations contradict the gas surface density threshold for star formation inferred from Halpha radial profiles of galaxies. We run smoothed particle hydrodynamics simulations of isolated disk galaxies with fiducial star formation prescriptions and show that over-densities owing to the presence of spiral structure can induce star formation in extended gas disks. For direct comparison with observations, we use the 3-D radiative transfer code Sunrise to create simulated FUV and K_s band images. We find that galaxies classified as Type I XUV disks are a natural consequence of spiral patterns, but we are unable to reproduce Type II XUV disks. We also compare our results to studies of the Kennicutt-Schmidt relation in outer disks.Comment: Published in Ap

    Interference in interacting quantum dots with spin

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    We study spectral and transport properties of interacting quantum dots with spin. Two particular model systems are investigated: Lateral multilevel and two parallel quantum dots. In both cases different paths through the system can give rise to interference. We demonstrate that this strengthens the multilevel Kondo effect for which a simple two-stage mechanism is proposed. In parallel dots we show under which conditions the peak of an interference-induced orbital Kondo effect can be split.Comment: 8 pages, 8 figure

    Kondo Correlations and the Fano Effect in Closed AB-Interferometers

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    We study the Fano-Kondo effect in a closed Aharonov-Bohm (AB) interferometer which contains a single-level quantum dot and predict a frequency doubling of the AB oscillations as a signature of Kondo-correlated states. Using Keldysh formalism, Friedel sum rule and Numerical Renormalization Group, we calculate the exact zero-temperature linear conductance GG as a function of AB phase ϕ\phi and level position ϵ\epsilon. In the unitary limit, G(ϕ)G(\phi) reaches its maximum 2e2/h2e^2/h at ϕ=π/2\phi=\pi/2. We find a Fano-suppressed Kondo plateau for G(ϵ)G(\epsilon) similar to recent experiments.Comment: 4 pages, 4 eps figure

    FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

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    Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ
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