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

Abstract

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.