SGNL: Scalable Low-Latency Gravitational Wave Detection Pipeline for Compact Binary Mergers

TL;DR

SGNL is a scalable, GPU-accelerated gravitational wave detection pipeline that reduces latency by 42% while maintaining sensitivity, using modern frameworks and a novel online inspiral detection algorithm.

Contribution

It introduces a modernized, modular framework for low-latency gravitational wave detection, integrating GPU acceleration and a new synchronized detection algorithm.

Findings

Median latency of 5.4 seconds, 42% faster than GstLAL.

Sensitivity comparable to GstLAL within uncertainties.

Flexible, modular pipeline architecture using Python and PyTorch.

Abstract

We present SGNL, a scalable, low-latency gravitational-wave search pipeline. It reimplements the core matched-filtering principles of the GstLAL pipeline within a modernized framework. The Streaming Graph Navigator library, a lightweight Python streaming framework, replaces GstLAL's GStreamer infrastructure, simplifying pipeline construction and enabling flexible, modular graph design. The filtering core is reimplemented in PyTorch, allowing SGNL to leverage GPU acceleration for improved computational scalability. We describe the pipeline architecture and introduce a novel implementation of the Low-Latency Online Inspiral Detection algorithm in which components are pre-synchronized to reduce latency. Results from a 40-day Mock Data Challenge show that SGNL's event recovery and sensitivity are consistent with GstLAL's within statistical and systematic uncertainties. Notably, SGNL achieves a median latency of 5.4 seconds, a 42\% reduction compared to GstLAL's 9.3 seconds.