arXiv subjects

Olivia Godwin

Publications and source records attributed to Olivia Godwin.

6 recordsLinked to original sources

arXiv2026

SGN: A python framework for stream-processing pipelines

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.

astro-ph.IM↗

arXiv2026

SGNAX: a unified matched-filter and excess-power pipeline for gravitational-wave detector characterization

We present SGNAX, an open-source pipeline for gravitational-wave detector characterization that delivers matched-filter and excess-power transient triggers from a single streaming dataflow graph. Built on the Stream Graph Navigator (sgn) framework, SGNAX unifies the multi-rate sine-Gaussian matched-filter search of snax and the multi-resolution Q-transform search of omicron in one Python-native package. Auxiliary channels from an interferometer are analyzed as parallel branches sharing data-read and whitening stages, reducing per-channel processing cost as channels are added. A full day of 16 kHz strain is analyzed in 14 minutes, and Q-transform processing is 2.8 times more efficient per channel at 32 channels than at one. Matched-filter correlations use PyTorch and run on CPU or GPU. Data sources include offline frame caches, shared-memory buffers, and the arrakis distribution service, with the same configuration supporting offline and online operation. The matched filter delivers triggers at about five seconds end-to-end latency, while the Q-transform operates at latencies of tens of seconds. Injection campaigns recover 99.4% of recoverable sine-Gaussian injections with parameters within the expected template mismatch and show broadband white-noise-burst recovery consistent with established event-trigger generators. On 24 hours of archival LIGO strain, SGNAX reproduces the omicron trigger population at 10--100 Hz, with trigger rates and SNRs agreeing to a few percent. On production auxiliary channels, it recovers the snax loud-feature population with 80% per-bin coincidence. The injection-calibrated reimplementation also reveals a multiband amplitude error in production snax that inflates reported SNRs below 25.6 Hz by factors of $\sqrt{2}$--2, for which we identify the mechanism and correction.

astro-ph.IM↗

arXiv2026

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

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 Stream 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 40 days of data 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 4.7 seconds, compared to 9.0 seconds for GstLAL.

astro-ph.IM↗

arXiv2026

GstLAL O4 Online Results Paper

Gravitational-wave observations of merging binary neutron stars and black holes are now routinely made by detectors in the Advanced LIGO-Virgo-KAGRA network. Neutron star binary systems may also produce detectable electromagnetic and particle emission over times scales ranging from seconds to years. Real-time gravitational-wave searches play a central role in enabling time-critical electromagnetic and/or neutrino follow-up observations. During the fourth observing run (O4) of the Advanced LIGO-Virgo-KAGRA network, multiple real-time searches operated continuously to identify candidate gravitational-wave events and publicly disseminate information about these discoveries. Here, the performance and results of the GstLAL real-time analysis are reported. The analysis is designed to identify candidates with low latency, high detection efficiency, and sustained operational uptime over long observing periods. Across O4, it produced initial candidate uploads with a median latency of 15.8 s while maintaining an effective uptime of 98% during the first two parts of the observing run. During the run, the analysis contributed to 250 candidates classified as astrophysically plausible, provided the first upload for 222 of these, and was the sole contributor for 75. Among Gravitational-Wave Transient Catalog events with a false-alarm rate below one per year, 88% were identified as significant in low latency and promoted for expert vetting and public dissemination. The low-latency astrophysical classifications agreed with the final catalog classifications for 93% of the events considered.

gr-qc↗

arXiv2026

Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit

We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.

astro-ph.IM↗

arXiv2026

Gauge Theoretic Signal Processing II: Zero-Latency Whitening for Early Warning Pipelines

Low-latency gravitational-wave search pipelines provide early-warning alerts for multimessenger astrophysical transients. Current pipelines whiten the data stream using acausal, linear-phase filters, which require a look-ahead buffer that introduces several seconds of algorithmic latency. Eliminating this latency requires causal, minimum-phase whitening filters using only past data. However, operating causal filters under non-stationary noise is non-trivial: the drifting power spectral density must be tracked without degrading the matched-filter signal-to-noise ratio (SNR), filter updates must preserve the minimum-phase condition, and the altered phase response must be compensated to maintain sky-localization accuracy. In Paper I we introduced a gauge theoretic signal processing framework and showed that the minimum-phase connection on the manifold of power spectra provides a geometrically exact update rule for causal filters. Here we validate that framework numerically and operationally, demonstrating that parallel transport along this connection strictly preserves the minimum-phase property while exactly conserving the matched-filter SNR. We numerically certify the flatness of this connection, showing that the optimal filter is a path-independent state function of the instantaneous noise. Through an injection campaign on O3 data with 15,347 binary black hole signals across the LIGO-Virgo network, we confirm that this architecture preserves the detection sensitivity and inter-detector timing and phase accuracy of the linear-phase baseline. Implementing the framework in the production sgnl pipeline reduces whitening latency by 1.0 s (33%) at a 4-second noise estimation cadence, confirmed in controlled tests and on live O3 replay data at production scale. Stride reduction experiments show that up to 91% of baseline trigger latency can be eliminated with sub-second pipeline cadence.

gr-qc↗