Virgo detector uses AI pipeline to classify gravitational-wave glitches

Researchers have developed VIGILant, an automated pipeline to classify and visualize glitches in the Virgo gravitational-wave detector. The system employs both tree-based machine learning models and a ResNet34 convolutional neural network, with the ResNet34 achieving a high F1 score of 0.9772 and accuracy of 0.9833. Deployed for daily use at the Virgo site since the O4c observing run, VIGILant provides an interactive dashboard to monitor glitch populations and detector behavior, aiding in the identification of low-confidence predictions that require further attention. AI

IMPACT Enhances scientific data analysis by automating glitch detection and classification in gravitational-wave observatories.

RANK_REASON The item describes a research paper detailing a new machine learning pipeline for a scientific instrument. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• decision tree
• O3b
• O4c
• Omicron
• random forest
• ResNet34
• Tiago Fernandes
• VIGILant
• Virgo
• XGBoost

• product
• infra
• paper

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