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Accelerated, scalable and reproducible AI-driven gravitational wave detection

Nature Astronomy volume 5, pages 1062–1068 (2021)Cite this article

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Abstract

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.

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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.

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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.

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Acknowledgements

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.

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Authors and Affiliations

  1. Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA

    E. A. Huerta, Ryan Chard, Ben Blaiszik & Ian Foster

  2. University of Chicago, Chicago, IL, USA

    E. A. Huerta, Maksim Levental, Ben Blaiszik & Ian Foster

  3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Asad Khan, Xiaobo Huang, Minyang Tian, Wei Wei, Maeve Heflin, Daniel S. Katz, Volodymyr Kindratenko & Dawei Mu

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Contributions

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.

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The authors declare no competing interests.

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

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Huerta, E.A., Khan, A., Huang, X. et al. Accelerated, scalable and reproducible AI-driven gravitational wave detection. Nat Astron 5, 1062–1068 (2021). https://doi.org/10.1038/s41550-021-01405-0

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