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How Many Times Should We Matched Filter Gravitational Wave Data? A Comparison of GstLAL's Online and Offline Performance

9 Pith papers cite this work. Polarity classification is still indexing.

9 Pith papers citing it
abstract

Searches for gravitational waves from compact binary coalescences employ a process called matched filtering, in which gravitational wave strain data is cross-correlated against a bank of waveform templates. Data from every observing run of the LIGO, Virgo, and KAGRA collaboration is typically analyzed in this way twice, first in a low-latency mode in which gravitational wave candidates are identified in near-real time, and later in a high-latency mode. Such high-latency analyses have traditionally been considered more sensitive, since background data from the full observing run is available for assigning significance to all candidates, as well as more robust, since they do not need to worry about keeping up with live data. In this work, we present a novel technique to use the matched filtering data products from a low-latency analysis and re-process them by assigning significances in a high-latency way, effectively removing the need to perform matched filtering a second time. To demonstrate the efficacy of our method, we analyze 38 days of LIGO and Virgo data from the third observing run (O3) using the GstLAL pipeline, and show that our method is as sensitive and reliable as a traditional high-latency analysis. Since matched filtering represents the vast majority of computing time for a traditional analysis, our method greatly reduces the time and computational burden required to produce the same results as a traditional high-latency analysis. Consequently, it has already been adopted by GstLAL for the fourth observing run (O4) of the LIGO, Virgo, and KAGRA collaboration.

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background 1 method 1

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years

2026 7 2025 2

representative citing papers

New Methods for Offline GstLAL Analyses

gr-qc · 2025-06-06 · conditional · novelty 5.0

New GstLAL offline methods reuse online matched-filtering products, merge with a heavy black hole search, revise the likelihood ratio and background estimation, and deliver a 50-100% sensitivity gain for high-mass events on a tested O3 data segment while improving reusability and reducing compute.

GstLAL O4 Online Results Paper

gr-qc · 2026-05-18 · unverdicted · novelty 3.0

GstLAL produced low-latency alerts for 250 astrophysically plausible gravitational-wave candidates during O4, providing the first upload for 222 and the sole upload for 75, with 88 percent of significant catalog events detected and 93 percent classification agreement.

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