Automatic classification pipeline for glitches in the Virgo detector
Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work introduces VIGILant, an automatic pipeline for classification and visualization of glitches in the Virgo detector. Using a curated dataset of Virgo O3b glitches, two machine learning approaches are evaluated: tree-based models (Decision Tree, Random Forest and XGBoost) using structured Omicron parameters, and Convolutional Neural Networks (ResNet) trained on spectrogram images. While tree-based models offer higher interpretability and fast training, the ResNet34 model achieved superior performance, reaching a F1 score of 0.9772 and accuracy of...
| Authors | Tiago Fernandes, Francesco Di Renzo, Antonio Onofre et al. |
|---|---|
| Primary category | gr-qc |
| Published | 15 April 2026 |
| Last updated at source | 20 August 2026 |
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