We discuss the extraction of information from detected binary black hole (BBH) coalescence gravitational waves by the ground-based interferometers LIGO and VIRGO, and by the space-based interferometer LISA. We focus on the merger phase that occurs after the gradual inspiral and before the ringdown. Our results are (i) if numerical relativity simulations have not produced template merger waveforms before BBH events are detected, one can study the merger waves using simple band-pass filters. For BBHs smaller than about ${40M}_{\ensuremath{\bigodot}}$ detected via their inspiral waves, the band-pass filtering signal-to-noise ratio indicates that the merger waves should typically be just barely visible in the noise for initial and advanced LIGO interferometers. (ii) We derive an optimized maximum-likelihood method for extracting a best-fit merger waveform from the noisy detector output; one ``perpendicularly projects'' this output onto a function space (specified using wavelets) that incorporates our (possibly sketchy) prior knowledge of the waveforms. An extension of the method allows one to extract the BBH's two independent waveforms from outputs of several interferometers. (iii) We propose a computational strategy for numerical relativists to pursue, if they successfully produce computer codes for generating merger waveforms, but if running the codes is too expensive to permit an extensive survey of the merger parameter space. In this case, for LIGO-VIRGO data analysis purposes, it would be advantageous to do a coarse survey of the parameter space aimed at exploring several qualitative issues and at determining the ranges of the several key parameters which we describe. (iv) A complete set of templates could be used to test the nonlinear dynamics of general relativity and to measure some of the binary's parameters via matched filtering. We estimate the number of bits of information obtainable from the merger waves (about 10--60 for LIGO-VIRGO, up to 200 for LISA), estimate the information loss due to template numerical errors or sparseness in the template grid, and infer approximate requirements on template accuracy and spacing.
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