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Data from a network of gravitational-wave detectors can be analyzed in coincidence to increase detection confidence and reduce nonstationarity of the background. We propose and explore a geometric algorithm to combine the data from a network of detectors. The algorithm makes optimal use of the variances and covariances that exist among the different parameters of a signal in a coincident detection of events. The new algorithm essentially associates with each trigger ellipsoidal regions in parameter space defined by the covariance matrix. Triggers from different detectors are deemed to be in coincidence if their ellipsoids have a nonzero overlap. Compared to an algorithm that uses uncorrelated windows separately for each of the signal parameters, the new algorithm greatly reduces the background rate thereby increasing detection efficien...
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