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GRAVITATIONAL WAVES

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Promising machine learning techniques can deduce the properties of merging black holes from gravitational wave signals a million times faster than current state-of-the-art methods.

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Fig. 1: Machine-learning-based source parameter estimation of gravitational wave signals.

References

  1. Abbott, R. et al. (LIGO Scientific Collaboration and Virgo Collaboration). Phys. Rev. X 11, 021053 (2021).

    MathSciNet  Google Scholar 

  2. Gabbard, H., Messenger, C., Heng, I. S., Tonolini, F. & Murray-Smith, R. Nat. Phys. https://doi.org/10.1038/s41567-021-01425-7 (2021).

  3. Veitch, J. & Vecchio, A. Phys. Rev. D 81, 062003 (2010).

    Article  ADS  Google Scholar 

  4. Smith, R. J. E., Ashton, G., Vajpeyi, A. & Talbot, C. Mon. Not. R. Astron. Soc. 498, 4492–4502 (2020).

    Article  ADS  Google Scholar 

  5. Maggiore, M. et al. J. Cosmol. Astropart. Phys. 03, 050 (2020).

    Article  ADS  Google Scholar 

  6. George, D. & Huerta, E. A. Phys. Lett. B 778, 64–70 (2018).

    Article  ADS  Google Scholar 

  7. Zevin, M. et al. Class. Quantum Grav. 34, 064003 (2017).

    Article  ADS  Google Scholar 

  8. Green, S. R. & Gair, J. Preprint at https://arxiv.org/abs/2008.03312 (2020).

  9. Romero-Shaw, I. et al. Astrophys. J. Lett. 903, L5 (2020).

    Article  ADS  Google Scholar 

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Correspondence to Rory Smith.

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Smith, R. OK Computer. Nat. Phys. 18, 9–11 (2022). https://doi.org/10.1038/s41567-021-01436-4

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