Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
General Relativity and Quantum Cosmology2024-06-25v2Instrumentation and Methods for AstrophysicsArtificial IntelligenceDistributed, Parallel, and Cluster Computing
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 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.
@article{arxiv.2012.08545,
title = {Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection},
author = {E. A. Huerta and Asad Khan and Xiaobo Huang and Minyang Tian and Maksim Levental and Ryan Chard and Wei Wei and Maeve Heflin and Daniel S. Katz and Volodymyr Kindratenko and Dawei Mu and Ben Blaiszik and Ian Foster},
journal= {arXiv preprint arXiv:2012.08545},
year = {2024}
}
Comments
17 pages, 5 figures; v2: 12 pages, 6 figures. Accepted to Nature Astronomy. See also the Behind the Paper blog in Nature Astronomy "https://astronomycommunity.nature.com/posts/from-disruption-to-sustained-innovation-artificial-intelligence-for-gravitational-wave-astrophysics"