Accelerated, scalable and reproducible AI-driven gravitational wave detection
Accelerated, scalable and reproducible AI-driven gravitational wave detection
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DOI：
10.1038/s41550-021-01405-0
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发表时间：
2021-07-05
期刊：
影响因子：
14.1
通讯作者：
Foster, Ian
中科院分区：
文献类型：
--
作者：
Huerta, E. A.;Khan, Asad;Foster, Ian
开发可重复使用的人工智能（AI）模型以供社区更广泛使用和严格验证，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程，将科学数据和学习中心（用于发布人工智能模型的存储库）与硬件加速学习（HAL）集群连接起来，使用 funcX 作为通用分布式计算服务。使用此工作流程，四个公开可用的人工智能模型的集合可以在 HAL 上运行，只需七分钟即可处理整整一个月（2017 年 8 月）的先进激光干涉仪引力波天文台数据，识别之前在此数据集中识别的所有四个二元黑洞合并，并报告没有错误分类。这种方法结合了人工智能、分布式计算和科学数据基础设施的进步，开辟了进行可重复、加速、数据驱动的发现的新途径。通过将人工智能模型存储库和超级计算集群相结合，只需 7 分钟即可分析整整一个月的先进 LIGO 数据，找到该数据集中先前识别的所有二元黑洞合并，并报告没有错误分类。
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.By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.