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 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.

开发可重复使用的人工智能 (AI) 模型以供社区更广泛地使用和严格验证，这有望为多信使天体物理学带来新的机遇。在这里，我们开发了一个工作流，将用于发布 AI 模型的存储库 Data and Learning Hub for Science 与硬件加速学习 (HAL) 集群连接起来，并使用 funcX 作为通用分布式计算服务。使用此工作流程，可以在 HAL 上运行四个公开可用的 AI 模型的集合，以在短短 7 分钟内处理整个月（2017 年 8 月）的先进激光干涉仪引力波天文台数据，识别先前确定的所有四个双黑洞合并在这个数据集中并报告没有错误分类。这种方法结合了人工智能的进步，