Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

TL;DR

This paper presents a workflow that leverages AI models and distributed computing to rapidly and reproducibly detect gravitational waves in large datasets, significantly reducing processing time.

Contribution

It introduces an integrated workflow connecting AI model repositories with distributed computing to enable fast, scalable, and reproducible gravitational wave detection.

Findings

Processed a month's data in 7 minutes using AI ensemble

Successfully identified all known black hole mergers in the dataset

No misclassifications reported in the detection process

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 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.