39 research outputs found

    Can induced gravity isotropize Bianchi I, V, or IX Universes?

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    We analyze if Bianchi I, V, and IX models in the Induced Gravity (IG) theory can evolve to a Friedmann--Roberson--Walker (FRW) expansion due to the non--minimal coupling of gravity and the scalar field. The analytical results that we found for the Brans-Dicke (BD) theory are now applied to the IG theory which has ωâ‰Ș1\omega \ll 1 (ω\omega being the square ratio of the Higgs to Planck mass) in a cosmological era in which the IG--potential is not significant. We find that the isotropization mechanism crucially depends on the value of ω\omega. Its smallness also permits inflationary solutions. For the Bianch V model inflation due to the Higgs potential takes place afterwads, and subsequently the spontaneous symmetry breaking (SSB) ends with an effective FRW evolution. The ordinary tests of successful cosmology are well satisfied.Comment: 24 pages, 5 figures, to be published in Phys. Rev. D1

    Transport properties of strongly correlated metals:a dynamical mean-field approach

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    The temperature dependence of the transport properties of the metallic phase of a frustrated Hubbard model on the hypercubic lattice at half-filling are calculated. Dynamical mean-field theory, which maps the Hubbard model onto a single impurity Anderson model that is solved self-consistently, and becomes exact in the limit of large dimensionality, is used. As the temperature increases there is a smooth crossover from coherent Fermi liquid excitations at low temperatures to incoherent excitations at high temperatures. This crossover leads to a non-monotonic temperature dependence for the resistance, thermopower, and Hall coefficient, unlike in conventional metals. The resistance smoothly increases from a quadratic temperature dependence at low temperatures to large values which can exceed the Mott-Ioffe-Regel value, hbar a/e^2 (where "a" is a lattice constant) associated with mean-free paths less than a lattice constant. Further signatures of the thermal destruction of quasiparticle excitations are a peak in the thermopower and the absence of a Drude peak in the optical conductivity. The results presented here are relevant to a wide range of strongly correlated metals, including transition metal oxides, strontium ruthenates, and organic metals.Comment: 19 pages, 9 eps figure

    Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

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    We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly aÎŒâ‰Ą(gΌ−2)/2a_\mu \equiv (g_\mu-2)/2. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency ωa\omega_a between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ω~pâ€Č{\tilde{\omega}'^{}_p} in a spherical water sample at 34.7∘^{\circ}C. The ratio ωa/ω~pâ€Č\omega_a / {\tilde{\omega}'^{}_p}, together with known fundamental constants, determines aÎŒ(FNAL)=116 592 040(54)×10−11a_\mu({\rm FNAL}) = 116\,592\,040(54)\times 10^{-11} (0.46\,ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both ÎŒ+\mu^+ and Ό−\mu^-, the new experimental average of aÎŒ(Exp)=116 592 061(41)×10−11a_\mu({\rm Exp}) = 116\,592\,061(41)\times 10^{-11} (0.35\,ppm) increases the tension between experiment and theory to 4.2 standard deviationsComment: 10 pages; 4 figure

    Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

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    The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine
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