SGN: A python framework for stream-processing pipelines
Instrumentation and Methods for Astrophysics
2026-07-03 v1 General Relativity and Quantum Cosmology
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.
Cite
@article{arxiv.2607.03575,
title = {SGN: A python framework for stream-processing pipelines},
author = {Yun-Jing Huang and Olivia Godwin and Chad Hanna and James Kennington and Jameson Rollins and Max Melching and Nathanael E Sovitzky and Aaron Viets and Madeline Wade and Zach Yarbrough and Yu-Kuang Chu and William Wyatt Phillips and Surabhi Sachdev and Rhiannon Udall},
journal= {arXiv preprint arXiv:2607.03575},
year = {2026}
}