加速、可扩展且可复现的AI驱动引力波探测
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
AI总结：
本研究搭建连接AI模型存储库与硬件加速学习集群的工作流，用集成AI模型7分钟处理完引力波天文台一个月的观测数据，准确识别全部已知双黑洞并合事件，实现可复现的加速引力波探测。
摘要：
可复用人工智能（AI）模型的开发若能供更广泛的群体使用并接受学界严格验证，有望在多信使天体物理学领域开辟新机遇。本研究开发了一套工作流，以funcX（通用分布式计算服务）为桥梁，连接用于发布AI模型的存储库——科学数据与学习中心（Data and Learning Hub for Science）与硬件加速学习（Hardware Accelerated Learning, HAL）集群。借助该工作流，由四个公开可用的AI模型组成的集成模型可在HAL集群上运行，仅用7分钟就处理完先进激光干涉引力波天文台（advanced Laser Interferometer Gravitational-Wave Observatory）整整一个月（2017年8月）的观测数据，识别出该数据集中此前已发现的全部四起双黑洞并合事件，且无误分类报告。该方法融合了AI、分布式计算与科学数据基础设施领域的进展，为开展可复现、加速的、数据驱动的科学发现开辟了新路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
