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.
References
Abbott, R. et al. (LIGO Scientific Collaboration and Virgo Collaboration). Phys. Rev. X 11, 021053 (2021).
Gabbard, H., Messenger, C., Heng, I. S., Tonolini, F. & Murray-Smith, R. Nat. Phys. https://doi.org/10.1038/s41567-021-01425-7 (2021).
Veitch, J. & Vecchio, A. Phys. Rev. D 81, 062003 (2010).
Smith, R. J. E., Ashton, G., Vajpeyi, A. & Talbot, C. Mon. Not. R. Astron. Soc. 498, 4492–4502 (2020).
Maggiore, M. et al. J. Cosmol. Astropart. Phys. 03, 050 (2020).
George, D. & Huerta, E. A. Phys. Lett. B 778, 64–70 (2018).
Zevin, M. et al. Class. Quantum Grav. 34, 064003 (2017).
Green, S. R. & Gair, J. Preprint at https://arxiv.org/abs/2008.03312 (2020).
Romero-Shaw, I. et al. Astrophys. J. Lett. 903, L5 (2020).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Rights and permissions
About this article
Cite this article
Smith, R. OK Computer. Nat. Phys. 18, 9–11 (2022). https://doi.org/10.1038/s41567-021-01436-4
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41567-021-01436-4
- Springer Nature Limited