论文标题
加速，可扩展和可重现的AI驱动引力波检测
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
论文作者
论文摘要
社区可重复使用的人工智能（AI）模型的开发和严格验证的开发有望在多门机天体物理学中释放新的机会。在这里，我们开发了一个工作流程，该工作流将数据和学习中心连接到科学（发布AI模型的存储库），并使用硬件加速学习（HAL）群集，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型号的合奏，以在短短七分钟内处理整个月的高级激光干涉仪重力波动台数据，并在此数据集中识别所有四个二进制黑洞合并，并识别所有四个二进制黑洞的合并，并报告没有错误分类。这种方法结合了AI，分布式计算和科学数据基础结构的进步，以开放新的途径，以进行可再现，加速，数据驱动的发现。
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.
