Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
This work provides a reproducible and accelerated method for gravitational wave detection, benefiting astrophysicists by speeding up data analysis and enabling broader validation of AI models.
This paper developed a workflow to process a month's worth of gravitational wave data using an ensemble of four AI models on the HAL cluster. The system identified all four previously known binary black hole mergers in the August 2017 dataset within seven minutes, with no misclassifications.
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.