870,463 research outputs found

    Backreaction on Moving Mirrors and Black Hole Radiation

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    We compute the effect of quantum mechanical backreaction on the spectrum of radiation in a dynamical moving mirror model, mimicking the effect of a gravitational collapse geometry. Our method is based on the use of a combined WKB and saddle-point approximation to implement energy conservation in the calculation of the Bogolyubov coefficients, in which we assume that the mirror particle has finite mass m. We compute the temperature of the produced radiation as a function of time and find that after a relatively short time, the temperature is reduced by a factor 1/2 relative to the standard result. We comment on the application of this method to two-dimensional dilaton gravity with a reflecting boundary, and conclude that the WKB approximation quickly breaks down due to the appearance of naked singularities and/or white hole space-times for the relevant WKB-trajectories.Comment: 12 pages, latex, 4 figure

    Estimating rBDπr_{B}^{D\pi} as an input to the determination of the CKM angle γ\gamma

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    The interference between Cabibbo-favoured and Cabibbo-suppressed BDπB\to D\pi decay amplitudes provides sensitivity to the CKM angle γ\gamma. The relative size of the interfering amplitudes is an important ingredient in the determination of γ\gamma. Using branching fractions from various BDhB\to Dh decays, and the measured value for rBDKr_{B}^{DK}, the magnitude of the amplitude ratio of B+D0π+B^+\to D^0\pi^+ and B+Dˉ0π+B^+\to \bar{D}^0\pi^+ decays is estimated to be rBDπ=0.0053±0.0007r_{B}^{D\pi} = 0.0053 \pm 0.0007.Comment: 4 pages, 4 figure

    Magnetron tuner has locking feature

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    Magnetron tuning arrangement features a means of moving a tuning ring axially within an anode cavity by a system of reduction gears engaging a threaded tuning shaft of lead screw. The shaft positions the tuning ring for the desired magnetron output frequency, and a washer prevents backlash

    Direct optical observation of walls and disclination effects in active photonic devices

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    Liquid crystal tunable Bragg Gratings defined in planar substrates via a laser patterning technique exhibit complex wavelength tuning. This tuning displays threshold points and hysteresis. These tuning features are shown to be a manifestation of physical processes occurring in the confined geometry of our tunable devices. Such physical processes include the formation and removal of line disclinations and an associated wall. We discuss the effect of walls in the liquid crystal with regards to voltage tuning characteristics and whether they may allow faster wavelength tuning

    Measuring Fine Tuning In Supersymmetry

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    The solution to fine tuning is one of the principal motivations for supersymmetry. However constraints on the parameter space of the Minimal Supersymmetric Standard Model (MSSM) suggest it may also require fine tuning (although to a much lesser extent). To compare this tuning with different extensions of the Standard Model (including other supersymmetric models) it is essential that we have a reliable, quantitative measure of tuning. We review the measures of tuning used in the literature and propose an alternative measure. We apply this measure to several toy models and the MSSM with some intriguing results.Comment: Submitted for the SUSY07 proceeding

    Longitudinal Cavity Mode Referenced Spline Tuning for Widely Tunable MG-Y Branch Semiconductor Laser

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    This paper presents a novel method for wavelength-continuous tuning of a MG-Y-Branch Laser that possesses an intrinsic self-calibration capability. The method utilizes the measured characteristic output power pattern caused by the internal longitudinal cavity modes of the laser device to calibrate a set of cubical spline curves. The spline curves are then used to generate the tuning currents for the two reflector sections and the phase section of the laser from an intermediate tuning control parameter. A calibration function maps the desired laser wavelength to the intermediate tuning parameter, thus enabling continuous tuning with high accuracy

    A precision study of the fine tuning in the DiracNMSSM

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    Recently the DiracNMSSM has been proposed as a possible solution to reduce the fine tuning in supersymmetry. We determine the degree of fine tuning needed in the DiracNMSSM with and without non-universal gaugino masses and compare it with the fine tuning in the GNMSSM. To apply reasonable cuts on the allowed parameter regions we perform a precise calculation of the Higgs mass. In addition, we include the limits from direct SUSY searches and dark matter abundance. We find that both models are comparable in terms of fine tuning, with the minimal fine tuning in the GNMSSM slightly smaller.Comment: 20 pages + appendices, 10 figure

    Heavy superpartners with less tuning from hidden sector renormalisation

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    In supersymmetric extensions of the Standard Model, superpartner masses consistent with collider bounds typically introduce significant tuning of the electroweak scale. We show that hidden sector renormalisation can greatly reduce such a tuning if the supersymmetry breaking, or mediating, sector runs through a region of strong coupling not far from the weak scale. In the simplest models, only the tuning due to the gaugino masses is improved, and a weak scale gluino mass in the region of 5 TeV may be obtained with an associated tuning of only one part in ten. In models with more complex couplings between the visible and hidden sectors, the tuning with respect to sfermions can also be reduced. We give an example of a model, with low scale gauge mediation and superpartner masses allowed by current LHC bounds, that has an overall tuning of one part in twenty.Comment: 18 pages, 6 figure

    Parameter-tuning Networks: Experiments and Active Walk Model

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    The tuning process of a large apparatus of many components could be represented and quantified by constructing parameter-tuning networks. The experimental tuning of the ion source of the neutral beam injector of HT-7 Tokamak is presented as an example. Stretched-exponential cumulative degree distributions are found in the parameter-tuning networks. An active walk model with eight walkers is constructed. Each active walker is a particle moving with friction in an energy landscape; the landscape is modified by the collective action of all the walkers. Numerical simulations show that the parameter-tuning networks generated by the model also give stretched exponential functions, in good agreement with experiments. Our methods provide a new way and a new insight to understand the action of humans in the parameter-tuning of experimental processes, is helpful for experimental research and other optimization problems.Comment: 4 pages, 5 figure

    Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

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    Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed
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