Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Accelerated, scalable and reproducible AI-driven gravitational wave detection


The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month’s worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Gravitational wave detection workflow with AI ensemble.
Fig. 2: Speed, sensitivity and scalability of AI ensemble.
Fig. 3: Spectrograms and neural network response to gravitational waves.
Fig. 4: Receiver operating characteristic curve of AI ensemble.
Fig. 5: DLHub architecture.
Fig. 6: Throughput of DLHub + HAL architecture.

Data availability

Advanced LIGO data used in this manuscript are open-source and readily available at the Gravitational Wave Open Science Center40. Modelled waveforms used to train, validate and test our AI models were produced using the open-source PyCBC library47. The waveform family used was SEOBNRv348. The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.

Code availability

All the required software to reproduce our results, encompassing AI models and post-processing scripts, are open-source and readily available at the DLHub and may be found at ref. 49. Software to produce waveforms at scale in high-performance computing platforms with PyCBC may be provided upon request.


  1. Abbott, B. P. et al. Observation of gravitational waves from a binary black hole merger. Phys. Rev. Lett. 116, 061102 (2016).

    Article  ADS  MathSciNet  Google Scholar 

  2. Abbott, B. P. et al. GWTC-1: a gravitational-wave transient catalog of compact binary mergers observed by LIGO and Virgo during the first and second observing runs. Phys. Rev. X 9, 031040 (2019).

    Google Scholar 

  3. Abbott, R. et al. GWTC-2: compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run. Preprint at (2020).

  4. Abbott, R. et al. Population properties of compact objects from the second LIGO–Virgo gravitational-wave transient catalog. Astrophys. J. Lett. 913, L7 (2021).

    Article  ADS  Google Scholar 

  5. Soares-Santos, M. et al. First measurement of the Hubble constant from a dark standard siren using the Dark Energy Survey galaxies and the LIGO/Virgo binary–black-hole merger GW170814. Astrophys. J. Lett. 876, L7 (2019).

    Article  ADS  Google Scholar 

  6. Abbott, B. P. et al. A gravitational-wave standard siren measurement of the Hubble constant. Nature 551, 85–88 (2017).

    Article  ADS  Google Scholar 

  7. Schutz, B. F. Determining the Hubble constant from gravitational wave observations. Nature 323, 310–311 (1986).

    Article  ADS  Google Scholar 

  8. Berti, E., Yagi, K. & Yunes, N. Extreme gravity tests with gravitational waves from compact binary coalescences: (I) inspiral–merger. Gen. Relativ. Gravit. 50, 46 (2018).

    Article  ADS  MathSciNet  Google Scholar 

  9. Abbott, B. et al. Tests of general relativity with the binary black hole signals from the LIGO-Virgo catalog GWTC-1. Phys. Rev. D 100, 104036 (2019).

    Article  ADS  Google Scholar 

  10. Radice, D. et al. Dynamical mass ejection from binary neutron star mergers. Mon. Not. R. Astron. Soc. 460, 3255–3271 (2016).

    Article  ADS  Google Scholar 

  11. Metzger, B. D. Kilonovae. Living Rev. Relativ. 23, 1 (2020).

    Article  ADS  Google Scholar 

  12. Huerta, E. A. et al. BOSS-LDG: a novel computational framework that brings together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to accelerate gravitational wave discovery. In Proc. IEEE 13th International Conference on e-Science 335–344 (2017).

  13. Huerta, E. A., Haas, R., Jha, S., Neubauer, M. & Katz, D. S. Supporting high-performance and high-throughput computing for experimental science. Comput. Softw. Big Sci. 3, 5 (2019).

    Article  Google Scholar 

  14. Weitzel, D. et al. Data access for LIGO on the OSG. In Proc. PEARC17: Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (eds Hart, D. L. & Dahan, M.) (Association for Computing Machinery, 2017).

  15. Asch, M. et al. Big data and extreme-scale computing: pathways to convergence – toward a shaping strategy for a future software and data ecosystem for scientific inquiry. Int. J. High Perform. Comput. Appl. 32, 435–479 (2018).

    Article  Google Scholar 

  16. Huerta, E. A. et al. Enabling real-time multi-messenger astrophysics discoveries with deep learning. Nat. Rev. Phys. 1, 600–608 (2019).

    Article  Google Scholar 

  17. George, D. & Huerta, E. A. Deep neural networks to enable real-time multimessenger astrophysics. Phys. Rev. D 97, 044039 (2018).

    Article  ADS  Google Scholar 

  18. George, D. & Huerta, E. A. Deep learning for real-time gravitational wave detection and parameter estimation: results with advanced LIGO data. Phys. Lett. B 778, 64–70 (2018).

    Article  ADS  Google Scholar 

  19. Gabbard, H., Williams, M., Hayes, F. & Messenger, C. Matching matched filtering with deep networks for gravitational-wave astronomy. Phys. Rev. Lett. 120, 141103 (2018).

    Article  ADS  Google Scholar 

  20. Lin, Y.-C. & Wu, J.-H. P. Detection of gravitational waves using Bayesian neural networks. Phys. Rev. D 103, 063034 (2021).

    Article  ADS  Google Scholar 

  21. Wang, H., Wu, S., Cao, Z., Liu, X. & Zhu, J.-Y. Gravitational-wave signal recognition of LIGO data by deep learning. Phys. Rev. D 101, 104003 (2020).

    Article  ADS  MathSciNet  Google Scholar 

  22. Zevin, M. et al. Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science. Class. Quantum Gravity 34, 064003 (2017).

    Article  ADS  Google Scholar 

  23. Torres-Forné, A., Cuoco, E., Font, J. A. & Marquina, A. Application of dictionary learning to denoise LIGO’s blip noise transients. Phys. Rev. D 102, 023011 (2020).

    Article  ADS  Google Scholar 

  24. Nakano, H. et al. Comparison of various methods to extract ringdown frequency from gravitational wave data. Phys. Rev. D 99, 124032 (2019).

    Article  ADS  Google Scholar 

  25. Fan, X., Li, J., Li, X., Zhong, Y. & Cao, J. Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors. Sci. China Phys. Mech. Astron. 62, 969512 (2019).

    Article  ADS  Google Scholar 

  26. Deighan, D. S., Field, S. E., Capano, C. D. & Khanna, G. Genetic-algorithm-optimized neural networks for gravitational wave classification. Neural Comput. & Applic. (2020).

  27. Miller, A. L. et al. How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search? Phys. Rev. D 100, 062005 (2019).

    Article  ADS  Google Scholar 

  28. Krastev, P. G. Real-time detection of gravitational waves from binary neutron stars using artificial neural networks. Phys. Lett. B 803, 135330 (2020).

    Article  MathSciNet  Google Scholar 

  29. Schäfer, M. B., Ohme, F. & Nitz, A. H. Detection of gravitational-wave signals from binary neutron star mergers using machine learning. Phys. Rev. D 102, 063015 (2020).

    Article  ADS  Google Scholar 

  30. Khan, A., Huerta, E. & Das, A. Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. Phys. Lett. B 808, 135628 (2020).

    Article  MathSciNet  Google Scholar 

  31. Dreissigacker, C., Sharma, R., Messenger, C., Zhao, R. & Prix, R. Deep-learning continuous gravitational waves. Phys. Rev. D 100, 044009 (2019).

    Article  ADS  Google Scholar 

  32. Beheshtipour, B. & Papa, M. A. Deep learning for clustering of continuous gravitational wave candidates. Phys. Rev. D 101, 064009 (2020).

    Article  ADS  MathSciNet  Google Scholar 

  33. Skliris, V., Norman, M. R. K. & Sutton, P. J. Real-time detection of unmodeled gravitational-wave transients using convolutional neural networks. Preprint at (2020).

  34. Khan, S. & Green, R. Gravitational-wave surrogate models powered by artificial neural networks. Phys. Rev. D 103, 064015 (2021).

    Article  ADS  MathSciNet  Google Scholar 

  35. Chua, A. J. K., Galley, C. R. & Vallisneri, M. Reduced-order modeling with artificial neurons for gravitational-wave inference. Phys. Rev. Lett. 122, 211101 (2019).

    Article  ADS  Google Scholar 

  36. Wei, W. & Huerta, E. A. Deep learning for gravitational wave forecasting of neutron star mergers. Phys. Lett. B 816, 136185 (2021).

    Article  MathSciNet  Google Scholar 

  37. Wei, W. et al. Deep learning with quantized neural networks for gravitational wave forecasting of eccentric compact binary coalescence. Preprint at (2020).

  38. Cuoco, E. et al. Enhancing gravitational-wave science with machine learning. Mach. Learn. Sci. Technol. 2, 011002 (2021).

    Article  Google Scholar 

  39. Wei, W., Khan, A., Huerta, E. A., Huang, X. & Tian, M. Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers. Phys. Lett. B 812, 136029 (2021).

    Article  MathSciNet  Google Scholar 

  40. Vallisneri, M., Kanner, J., Williams, R., Weinstein, A. & Stephens, B. The LIGO Open Science Center. J. Phys. Conf. Ser. 610, 012021 (2015).

    Article  Google Scholar 

  41. Kindratenko, V. et al. HAL: computer system for scalable deep learning. In Proc. PEARC20: Practice and Experience in Advanced Research Computing 41–48 (Association for Computing Machinery, 2020).

  42. Li, Z. et al. DLHub: simplifying publication, discovery, and use of machine learning models in science. J. Parallel Distrib. Comput. 147, 64–76 (2021).

    Article  Google Scholar 

  43. Chard, R. et al. DLHub: model and data serving for science. In Proc. IEEE International Parallel and Distributed Processing Symposium 283–292 (2019).

  44. Allen, M. G. et al. ESCAPE – addressing Open Science challenges. Preprint at (2020).

  45. Chard, R. et al. FuncX: a federated function serving fabric for science. In Proc. 29th International Symposium on High-Performance Parallel and Distributed Computing 65–76, (Association for Computing Machinery, 2020).

  46. Chard, K., Tuecke, S. & Foster, I. Efficient and secure transfer, synchronization, and sharing of big data. IEEE Cloud Comput. 1, 46–55 (2014).

    Article  Google Scholar 

  47. Nitz, A. H. et al. PyCBC. Free and open software to study gravitational waves. (2021).

  48. Pan, Y. et al. Inspiral-merger-ringdown waveforms of spinning, precessing black-hole binaries in the effective-one-body formalism. Phys. Rev. D 89, 084006 (2014).

    Article  ADS  Google Scholar 

  49. Huerta, E. A. et al. AI-driven Gravitational Wave Detection (Data and Learning Hub for Science, 2021);

  50. van den Oord, A. et al. WaveNet: a generative model for raw audio. In Proc. 9th ISCA Speech Synthesis Workshop (eds Bonafont, A. & Prahallad, K.) 135–136 (2016).

Download references


We gratefully acknowledge NSF awards OAC-1931561 and OAC-1934757 (E.A.H.), OAC-1931306 (B.B.) and OAC-2004894 (I.F.). E.A.H. gratefully acknowledges the Innovative and Novel Computational Impact on Theory and Experiment project ‘Multi-Messenger Astrophysics at Extreme Scale in Summit’. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract no. DE-AC05-00OR22725. This work used resources supported by the NSF’s Major Research Instrumentation program, the HAL cluster (grant no. OAC-1725729), as well as by the University of Illinois at Urbana-Champaign. DLHub is based upon work initially supported by Laboratory Directed Research and Development funding from Argonne National Laboratory, provided by the Director, Office of Science, of the DOE under contract no. DE-AC02-06CH11357. We thank NVIDIA for their continued support.

Author information

Authors and Affiliations



E.A.H. led this work and coordinated the writing of this manuscript. A.K. developed and trained the AI ensemble, and also developed the software to scale this analysis over the entire HAL cluster and to post-process the output of the AI ensemble to estimate sensitivity and perform error analysis. X.H., M.T., M.H. and W.W. prepared the datasets and software used for training, testing and inference and conducted an independent inference study to ascertain the reproducibility of our AI ensemble. D.M. and V.K. optimized the HAL cluster, at both the hardware and software level, to maximize its throughput for AI training and inference at scale. B.B., I.F. and D.S.K. informed and guided the construction of the DLHub → funcX → HAL workflow. M.L. and R.C. ran an independent AI analysis using the workflow to establish the reproducibility and scalability of the results. All authors contributed to developing the ideas and to writing and reviewing this manuscript.

Corresponding author

Correspondence to E. A. Huerta.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Astronomy thanks Elena Cuoco, Plamen Krastev and Linqing Wen for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huerta, E.A., Khan, A., Huang, X. et al. Accelerated, scalable and reproducible AI-driven gravitational wave detection. Nat Astron 5, 1062–1068 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing