发表机构
California Institute of Technology; The Pennsylvania State University; Concordia University Wisconsin; Kenyon College（加州理工学院; 宾夕法尼亚州立大学; 威斯康星协和大学; 肯扬学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍SGN这一轻量级Python框架用于构建流数据应用，通过连接组件成有向无环图在事件循环中运行，其时间序列扩展引入信号处理方法，为引力波搜索管道提供基础。
AI 中文摘要
我们展示了流图导航器（SGN），一个用于构建流数据应用的轻量级Python框架。在SGN中，流处理管道通过将计算组件连接成在事件循环内运行的有向无环图来构建。SGN库的时间序列扩展SGN-TS引入了处理时间序列数据的信号处理方法。SGN和SGN-TS共同为SGNL（一个匹配滤波引力波搜索管道）提供了基础，并被低延迟引力波数据分析基础设施中的多个项目采用，作为未来引力波观测的可扩展和可维护框架。
英文摘要
We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.
