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S. Klimenko

Publications and source records attributed to S. Klimenko.

20 records · Page 2Linked to original sources

Constraint Likelihood analysis for a network of gravitational wave detectors

We propose a coherent method for the detection and reconstruction of gravitational wave signals for a network of interferometric detectors. The method is derived using the likelihood functional for unknown signal waveforms. In the standard approach, the global maximum of the likelihood over the space of waveforms is used as the detection statistic. We identify a problem with this approach. In the case of an aligned pair of detectors, the detection statistic depends on the cross-correlation between the detectors as expected, but this dependence dissappears even for infinitesimally small misalignments. We solve the problem by applying constraints on thelikelihood functional and obtain a new class of statistics. The resulting method can be applied to the data from a network consisting of any number of detectors with arbitrary detector orientations. The method allows us reconstruction of the source coordinates and the waveforms of two polarization components of a gravitational wave. We study the performance of the method with numerical simulation and find the reconstruction of the source coordinates to be more accurate than in the standard approach.

gr-qc

A cross-correlation technique in wavelet domain for detection of stochastic gravitational waves

Stochastic gravitational waves (SGW) can be detected by measuring a cross-correlation of two or more gravitational wave (GW) detectors. In this paper we describe an optimal SGW search technique in the wavelet domain. It uses a sign correlation test, which allows calculation of the cross- correlation significance for non-Gaussian data. We also address the problem of correlated noise for the GW detectors. A method that allows calculation of the cross-correlation variance, when data is affected by correlated noise, is developed. As a part of the optimal search technique a robust estimator for detector noise spectral amplitude is introduced. It is not sensitive to outliers and allows application of the search technique to non-stationary data.

gr-qc