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Parameter estimates in binary black hole collisions using neural networks. (English) Zbl 1380.83129
Summary: We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given gravitational wave (GW) strains. In this analysis, the ANN is trained with a sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GWs generated also with simulations for given mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the interpolation and extrapolation regimes. We present the results for noise free signals and signals contaminated with Gaussian noise, in order to foresee the dependence of the method accuracy in terms of the signal to noise ratio.

MSC:
83C57 Black holes
83C35 Gravitational waves
83-08 Computational methods for problems pertaining to relativity and gravitational theory
83C10 Equations of motion in general relativity and gravitational theory
83C75 Space-time singularities, cosmic censorship, etc.
62P35 Applications of statistics to physics
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