@article{b21be5c4588343829262fe070800620b,
title = "Accelerated, scalable and reproducible AI-driven gravitational wave detection",
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{\textquoteright}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.",
author = "Huerta, {E. A.} and Asad Khan and Xiaobo Huang and Minyang Tian and Maksim Levental and Ryan Chard and Wei Wei and Maeve Heflin and Katz, {Daniel S.} and Volodymyr Kindratenko and Dawei Mu and Ben Blaiszik and Ian Foster",
note = "Funding Information: This paper brings together several key elements to accelerate deep learning research. We showcase how to combine cyberin-frastructure funded by the National Science Foundation (NSF) and the Department of Energy (DOE) to release state-of-the-art, production-scale, neural network models for gravitational wave detection. The framework DLHub → funcX → HAL provides the means to enable open-source, accelerated, deep learning, gravitational wave data analysis. This approach will empower the broader community to readily process open-source LIGO data with minimal computational resources. Going forward, this approach may Funding Information: 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 {\textquoteleft}Multi-Messenger Astrophysics at Extreme Scale in Summit{\textquoteright}. 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{\textquoteright}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. Funding Information: The outstanding aspect of this work is the consolidation of these five disparate elements into a unified framework for end-to-end, AI-driven gravitational wave detection. This type of big-data, open-science research is part of a global project that aims to harness AI and advanced cyberinfrastructure to enable innovation in data-intensive research through new modes of data-driven discovery. Examples are the NSF Harnessing the Data Revolution and the DOE FAIR (Findable, Accessible, Interoperable, and Reusable) projects in the United States; the European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures (ESCAPE) project; and the European Open Science Cloud (EOSC)44. Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer Nature Limited.",
year = "2021",
month = oct,
doi = "10.1038/s41550-021-01405-0",
language = "English (US)",
volume = "5",
pages = "1062--1068",
journal = "Nature Astronomy",
issn = "2397-3366",
publisher = "Nature Publishing Group",
number = "10",
}