AI Insight
This paper introduces Stream Graph Navigator (SGN), a Python framework designed for building streaming data processing applications using directed acyclic graphs within an event loop. The framework includes SGN-TS, an extension for time series signal processing, and serves as the foundation for SGNL, a matched-filtering pipeline used in gravitational-wave detection. SGN is being adopted across multiple low-latency gravitational-wave data analysis projects as a maintainable and extensible infrastructure solution.
Why it matters
This framework provides crucial infrastructure for real-time gravitational-wave detection, enabling faster analysis of astronomical events like black hole mergers and neutron star collisions. The lightweight, modular design makes it accessible for other scientific applications requiring real-time stream processing of complex data.
Understand the Science
arXiv:2607.03575v2 Announce Type: replace
Abstract: 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.
Source: SGN: A python framework for stream-processing pipelines