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Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

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arxiv 2012.08545 v2 pith:LBJDTUOK submitted 2020-12-15 gr-qc astro-ph.IMcs.AIcs.DC

E. A. Huerta , Asad Khan , Xiaobo Huang , Minyang Tian , Maksim Levental , Ryan Chard , Wei Wei , Maeve Heflin
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Daniel S. Katz Volodymyr Kindratenko Dawei Mu Ben Blaiszik Ian Foster
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classification gr-qcastro-ph.IMcs.AIcs.DC
keywords accelerateddatafourmodelscomputingdistributedlearningreproducible
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms

    gr-qc 2025-09 conditional novelty 5.0 of 10

    AresGW model 1's injection detection count at a false-alarm rate of 1/month varies with noise dataset by up to 39% coefficient of variation, while sensitive distance varies by only a few percent.

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