Unified matched-filter and excess-power pipeline for gravitational-wave detector characterization

arXiv:2608.09804 · astro-ph.IM · Submitted 2026-08-10 · Read on arXiv

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Introduction to the show: ident: Astrophysics. The day in astrophysics: observations, models and what the data forces.

Vera: I'm Vera.

Jocelyn: And I'm Jocelyn, here with Subrahmanyan.

Vera: We're looking at the paper: "Unified matched-filter and excess-power pipeline for gravitational-wave detector characterization".

Jocelyn: Here's the gist of the paper. sgnax presents sgnax, an open-source pipeline for gravitational-wave detector characterization that delivers both matched-filter and excess-power transient triggers from a single streaming dataflow graph The paper introduces sgnax as a unified Python package that subsumes both the multi-rate sine-Gaussian matched filter search of snax and the multi-resolution Q-transform excess-power search of omicron into one framework This unification allows for a single configuration object, output schema, and dependency tree to manage both analyses.

Vera: Pipeline Architecture sgnax is built on the Stream Graph Navigator (sgn) dataflow framework, which is described as a directed graph of elements connected through named pads The pipeline draws elements from four sgn ecosystem packages, including sgn-ts for time-series elements and sgn-ligo and sgn-arrakis for data sources.

Jocelyn: A single invocation processes an arbitrary list of channels from a single interferometer through one pipeline, with each channel's final trigger stream routed to its own pad on a single shared HDF5FeatureSink.

Vera: The two pipelines share a configuration object, DataSourceInfo, which specifies the data acquisition stage and allows both analyses to consume any of three data-source backends: offline frame caches, the low-latency shared-memory buffer, and the arrakis distribution service.

Paper summary: Vera: So, to recap, this paper introduces sgnax as an open-source pipeline that delivers both matched filter and excess power transient triggers from a single streaming dataflow graph. The core thesis is unification.

Jocelyn: They combine the multi-rate sine-Gaussian matched filter search of snax and the multi-resolution Q-transform excess-power search of omicron into one framework. This allows for a single configuration object, output schema, and dependency tree to manage both analyses together.

Subrahmanyan: The motivation for this rewrite is fourfold. First, the operational cost of maintaining snax as a thin Python layer over the gstreamer-one point zero dataflow has grown with each upstream release <ref:2608.09804#pg1>.

Vera: Second, the sgn framework offers a typed, native-python alternative whose elements can be composed without a C-level pipeline backbone. That’s a big deal for maintainability and flexibility.

Jocelyn: Third, omicron requires a heavy set of external libraries and two separate pipelines to run it. This unified approach solves that dependency problem by putting both analyses in one place.

Subrahmanyan: And the motivation is also about closing a long-standing latency gap by allowing Q-transform triggers to reach commissioners within tens of seconds of acquisition.

Vera: The pipeline itself is built on the Stream Graph Navigator framework, which uses a directed graph of elements connected through named pads. It pulls elements from sgn ecosystem packages like sgn-ts for time series and data sources like sgn-ligo and arrakis.

Jocelyn: A single invocation processes an arbitrary list of channels through one pipeline. Each channel's final trigger stream is routed to its own pad on a single shared HDF5 feature sink.

Subrahmanyan: The configuration object, DataSourceInfo, lets both analyses consume any of three data-source backends: offline frame caches, the low latency shared memory buffer, and the arrakis distribution service.

Vera: So the point is that they’ve created a unified package that handles both analyses with one set of tools.

Jocelyn: It allows for a single configuration object, output schema, and dependency tree to manage both analyses. It makes setting up these complex characterization workflows much simpler.

Subrahmanyan: This moves the work from being two separate, heavy pipelines to one cohesive system managed by a single interface.

Conclusion: Vera: So, looking at the title "Unified matched-filter and excess-power pipeline for gravitational-wave detector characterization," it really sums up what they did. They took two established methods and merged them into one unified pipeline.

Jocelyn: It’s about taking the operational burden off people who are trying to characterize detectors, letting them manage one package instead of two different ones.

Subrahmanyan: The implication is that for those working on detector characterization, this framework offers a single place to go for both matched filter and excess power searches.

Vera: It specifically addresses the latency issue by aiming to get Q-transform triggers to commissioners within tens of seconds of acquisition, which is a key operational goal.

Jocelyn: And the work establishes that the Q-plane construction and excess-power statistic reproduce the omicron trigger population on archival strain data to within about twenty percent in every frequency band that hosts scattered light and other transient noise classes <ref:2608.09804#pg2>.

Subrahmanyan: So, what this means for cosmology or structure formation is that we have a robust tool now for extracting those specific types of signals from detector data, which is crucial input for those larger cosmological models.

Vera: The open-source release provides the implementation and validation suite needed to migrate existing workflows. It’s there to help people move away from the older gstreamer-one point zero dependency toward this native Python approach <ref:2608.09804#pg1>.

Jocelyn: It closes a long-standing latency gap by making those Q-transform triggers more accessible in time for rapid follow-up or monitoring activities.

Department of Physics and Astronomy, Louisiana State University · LIGO Laboratory, California Institute of Technology Department of Physics, The Pennsylvania State University Institute for Gravitation and the Cosmos University of Rhode Island

astro-ph.IM

Submitted: 2026-08-10

Updated: 2026-10-27

Comments: 27 pages, 16 figures, 5 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 100/100

The gist: sgnax presents sgnax, an open-source pipeline for gravitational-wave detector characterization that delivers both matched-filter and excess-power transient triggers from a single streaming dataflow

Terminology

Summary

sgnax presents sgnax, an open-source pipeline for gravitational-wave detector characterization that delivers both matched-filter and excess-power transient triggers from a single streaming dataflow graph The paper introduces sgnax as a unified Python package that subsumes both the multi-rate sine-Gaussian matched filter search of snax and the multi-resolution Q-transform excess-power search of omicron into one framework This unification allows for a single configuration object, output schema, and dependency tree to manage both analyses.

Pipeline Architecture

sgnax is built on the Stream Graph Navigator (sgn) dataflow framework, which is described as a directed graph of elements connected through named pads The pipeline draws elements from four sgn ecosystem packages, including sgn-ts for time-series elements and sgn-ligo and sgn-arrakis for data sources. A single invocation processes an arbitrary list of channels from a single interferometer through one pipeline, with each channel's final trigger stream routed to its own pad on a single shared HDF5FeatureSink. The two pipelines share a configuration object, DataSourceInfo, which specifies the data acquisition stage and allows both analyses to consume any of three data-source backends: offline frame caches, the low-latency shared-memory buffer, and the arrakis distribution service.

Algorithmic Implementation

sgnax provides two analysis modes through its command-line entry points: sgnax-extract, which performs the multi-rate sine-Gaussian matched filter search inherited from snax, and sgnax-qtransform, which performs the bisquare-windowed Qtransform excess-power search of omicron. The matched-filter pipeline uses a sliding-window matched-filter correlation against a bank of unit-norm sine-Gaussian templates, and the Qtransform pipeline reimplements the omicron algorithm as a single sgn transform element, QScan. Both pipelines are validated by injecting 3299 sine-Gaussian waveforms into a stationary Gaussian-noise auxiliary channel and recovering them with both modes.

Performance and Validation

The paper validates both analyses by comparing their performance against injections, showing that sgnax-extract recovers 99.4% of recoverable sine-Gaussian injections with parameters within the expected template mismatch. For the Q-transform, it matches 3130 of the 3299 injections within a coincidence window, also recovering 99.4% of the recoverable population. When compared to production omicron triggers on archival strain data, sgnax-qtransform reproduces the omicron trigger population to within ~20% in every frequency band that hosts scattered light and other transient noise classes.

Key Differences and Operational Advantages

The architectural differences from predecessors include replacing the gstreamer-1.0 dataflow with the in-process sgn graph and removing C-level dependencies. The Q-transform mode, when run with a 16 s chunk, shows a median end-to-end latency of 12 s, whereas the matched filter mode delivers triggers at a median end-to-end latency of 5.0 s. The single configuration object and unified output schema reduce the operational surface area by replacing two pipelines with one.

Conclusion

sgnax is intended to occupy the same operational role currently filled by snax and omicron for auxiliary channel transient extraction in gravitational-wave detector characterization workflows. The unified framework provides a single configuration object, output schema, and dependency tree, closing a long-standing latency gap by allowing Q-transform triggers to reach commissioners within tens of seconds of acquisition. The work establishes that the Q-plane construction and excess-power statistic reproduce the omicron trigger population on archival strain data to within ~20% in every frequency band that hosts scattered light and other transient noise classes of central importance to detector characterization. The accompanying open-source release provides the implementation, validation suite, and documentation needed to migrate existing detector-characterization workflows. The paper reports that the low-frequency SNR excess of the production snax deployment is localized in a multiband amplitude error in that deployment.

Improvements for AI systems

  1. Unified Dataflow Architecture: The sgnax dataflow framework allows arbitrary lists of auxiliary channels from a single interferometer to be analyzed as parallel branches of one graph rather than separate jobs, which improves efficiency by sharing one data-read and whitening front end and reducing per-channel processing cost.

  2. Multi-Modal Trigger Generation: sgnax delivers both matched-filter and excess-power transient triggers from a single streaming dataflow graph with a single configuration object, output schema, and dependency tree, unifying the analyses that previously required separate tools like snax and omicron.

  3. Low Latency Online Trigger Delivery: The sgnax-extract mode achieves an end-to-end latency of about five seconds by carrying no per-chunk buffering and delivering triggers at a measured end-to-end latency of about five seconds, which is comparable to the original snax achievement while maintaining low latency feature generation capability.

  4. Online Q Transform Deployment: The sgnax-qtransform mode can deliver Q-transform triggers to detector commissioners within tens of seconds of acquisition by consuming the low-latency shared-memory and arrakis data sources through the same configuration object used for offline runs, closing an operational gap with production batch workflows.

  5. Automated Workflow Generation: The sgnax-dagger entry point builds htcondor workflows via ezdag, isolating the dependency in an optional condor extra so that search entry points carry no htcondor requirement, enabling automated deployment of complex analyses.

Sources

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