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Parameter estimates in binary black hole collisions using neural networks

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Abstract

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.

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Acknowledgments

This research is supported by Grants CIC-UMSNH-4.9, CIC-UMSNH-4.23 and CONACyT 258726 (Fondo Sectorial de Investigación para la Educación).

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Authors and Affiliations

  1. Laboratorio de Inteligencia Artificial y Supercómputo, Instituto de Física y Matemáticas, Universidad Michoacana de San Nicolás de Hidalgo, 58040, Morelia, Michoacán, México

    M. Carrillo, M. Gracia-Linares, J. A. González & F. S. Guzmán

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  1. M. Carrillo
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  2. M. Gracia-Linares
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  3. J. A. González
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  4. F. S. Guzmán
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Correspondence to F. S. Guzmán.

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Carrillo, M., Gracia-Linares, M., González, J.A. et al. Parameter estimates in binary black hole collisions using neural networks. Gen Relativ Gravit 48, 141 (2016). https://doi.org/10.1007/s10714-016-2136-0

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