46,532 research outputs found

    Prediction of signal‐to‐noise ratio gain for passive higher‐order correlation detection of energy transients

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    In general, higher‐order correlation detectors perform well in passive detection for signals of high third‐ and fourth‐order moments. Previous studies by the authors have shown that the normalized third‐ and fourth‐order signal moments are reliable indicators of higher‐order correlation detector performance [Pflug et al. (1992b)]. For a deterministic energy transient of known moments through fourth order, it is possible to predict theoretically the amount of gain over an ordinary cross‐correlation detector for a bicorrelation or tricorrelation detector applied in a noise environment of known variance. In this paper, formulas that predict detector performance for passive detection at the minimum detectable level are derived. The noise is assumed to be stationary and zero mean with Gaussian correlation central ordinate probability density functions. To test the formulas, SNR detection and gain curves are generated using hypothesis testing and Monte Carlo simulations on a set of test signals. The test signals are created by varying the time width of a pulse‐like signal in a sampling window of fixed time duration, resulting in a set of test signals with varying signal moments. Good agreement is found between the simulated and theoretical results. The effects of observation time (length of detection window) and sampling interval on detector performance are also discussed and illustrated with computer simulations. The prediction formulas indicate that decreasing the observation time or the sampling interval (assuming the signal is sufficiently sampled and the detection window contains the entire signal) improves detection performance. However, the rate of improvement is different for the three detectors. The SNR required to achieve the minimum detectable level of detection performance at a given probability of false alarm (Pfa) decreases with the fourth root of the observation time and sampling interval for the cross‐correlation detector, the sixth root for the bicorrelation detector, and the eighth root for the tricorrelation detector. Relative detector performance also varies with Pfa. The probability of detection (Pd) for higher‐order detectors degrades less rapidly with decreasing Pfa than the Pd for ordinary correlations. Thus higher‐order correlators can be especially appropriate when a very low Pfa is required

    X-Ray Detection of Transient Magnetic Moments Induced by a Spin Current in Cu

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    We have used a MHz lock-in x-ray spectro-microscopy technique to directly detect changes of magnetic moments in Cu due to spin injection from an adjacent Co layer. The elemental and chemical specificity of x-rays allows us to distinguish two spin current induced effects. We detect the creation of transient magnetic moments of 3×10−53\times 10^{-5} ÎŒB\mu_\mathrm{B} on Cu atoms within the bulk of the 28 nm thick Cu film due to spin-accumulation. The moment value is compared to predictions by Mott's two current model. We also observe that the hybridization induced existing magnetic moments on Cu interface atoms are transiently increased by about 10% or 4×10−34\times 10^{-3} ÎŒB\mu_\mathrm{B}. This reveals the dominance of spin-torque alignment over Joule heat induced disorder of the interfacial Cu moments during current flow

    Anisotropic relaxation in NADH excited states studied by polarization-modulation pump-probe transient spectroscopy

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    We present the results of experimental and theoretical studies of fast anisotropic relaxation and rotational diffusion in the first electron excited state of biological coenzyme NADH in water-ethanol solutions. The experiments have been carried out by means of a novel polarization-modulation transient method and fluorescence polarization spectroscopy. For interpretation of the experimental results a model of the anisotropic relaxation in terms of scalar and vector properties of transition dipole moments and based on the Born-Oppenheimer approximation has been developed. The results obtained suggest that the dynamics of anisotropic rovibronic relaxation in NADH under excitation with 100~fs pump laser pulses can be characterised by a single vibration relaxation time τv\tau_v laying in the range 2--15~ps and a single rotation diffusion time τr\tau_r laying in the range 100--450~ps a subject of ethanol concentration. The dependence of the times τv\tau_v and τr\tau_r on the solution polarity (static permittivity) and viscosity has been determined and analyzed. Limiting values of an important parameter ⟹P2(cos⁥Ξ(t))⟩\langle P_2(\cos\theta(t))\rangle describing the rotation of the transition dipole moment in the course of vibrational relaxation has been determined from experiment as function of the ethanol concentration and analyzed.Comment: 14 pages, 13 figure

    A learning approach to the detection of gravitational wave transients

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    We investigate the class of quadratic detectors (i.e., the statistic is a bilinear function of the data) for the detection of poorly modeled gravitational transients of short duration. We point out that all such detection methods are equivalent to passing the signal through a filter bank and linearly combine the output energy. Existing methods for the choice of the filter bank and of the weight parameters rely essentially on the two following ideas: (i) the use of the likelihood function based on a (possibly non-informative) statistical model of the signal and the noise, (ii) the use of Monte-Carlo simulations for the tuning of parametric filters to get the best detection probability keeping fixed the false alarm rate. We propose a third approach according to which the filter bank is "learned" from a set of training data. By-products of this viewpoint are that, contrarily to previous methods, (i) there is no requirement of an explicit description of the probability density function of the data when the signal is present and (ii) the filters we use are non-parametric. The learning procedure may be described as a two step process: first, estimate the mean and covariance of the signal with the training data; second, find the filters which maximize a contrast criterion referred to as deflection between the "noise only" and "signal+noise" hypothesis. The deflection is homogeneous to the signal-to-noise ratio and it uses the quantities estimated at the first step. We apply this original method to the problem of the detection of supernovae core collapses. We use the catalog of waveforms provided recently by Dimmelmeier et al. to train our algorithm. We expect such detector to have better performances on this particular problem provided that the reference signals are reliable.Comment: 22 pages, 4 figure
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