GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run

Abstract

The frequent presence of non-Gaussian transient noise, or glitches, in gravitational-wave detector data can affect gravitational-wave searches, parameter estimation, and downstream analyses. To identify and mitigate transient noise near gravitational-wave candidates in a timely manner, the LIGO-Virgo-KAGRA Collaboration employs the Data Quality Report. In the fourth observing run, GSpyNetTree-O4 was deployed within this framework as a tool for glitch classification and event validation. We describe GSpyNetTree-O4 and the main developments relative to its predecessor, GSpyNetTree. The most important update was a new architecture that allowed the simultaneous identification of glitches and gravitational-wave signals when both were present in the same input. We also expanded and augmented the training set with examples in which simulated gravitational-wave signals overlapped with real glitches, and applied $60\mathrm{Hz}$ calibration corrections to better match the data expected during the fourth observing run. On test data, the low-mass, high-mass, and extremely high-mass classifiers identified $97.9\%$, $97.7\%$, and $95.4\%$ of glitches, respectively. Among samples without a glitch, including gravitational-wave-only and No Glitch samples, the classifiers correctly reported no data-quality issues in $97.1\%$, $96.6\%$, and $96.0\%$ of cases, respectively. We further assessed the robustness of GSpyNetTree-O4 on unseen glitch morphologies, a small set of Virgo glitches from the fourth observing run, and different choices of the $Q$-value used to construct the time-frequency inputs. GSpyNetTree-O4 was successfully deployed as a Data Quality Report tool and increased automation in gravitational-wave event validation workflows.

23 pages, 16 figures

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