Virgo Detector Characterization and Data Quality: results from the O3 run

Abstract

The Advanced Virgo detector has contributed with its data to the rapid growth of the number of detected gravitational-wave (GW) signals in the past few years, alongside the two Advanced LIGO instruments. First during the last month of the Observation Run 2 (O2) in August 2017 (with, most notably, the compact binary mergers GW170814 and GW170817), and then during the full Observation Run 3 (O3): an 11-months data taking period, between April 2019 and March 2020, that led to the addition of about 80 events to the catalog of transient GW sources maintained by LIGO, Virgo and now KAGRA. These discoveries and the manifold exploitation of the detected waveforms require an accurate characterization of the quality of the data, such as continuous study and monitoring of the detector noise sources. These activities, collectively named {\em detector characterization and data quality} or {\em DetChar}, span the whole workflow of the Virgo data, from the instrument front-end hardware to the final analyses. They are described in details in the following article, with a focus on the results achieved by the Virgo DetChar group during the O3 run. Concurrently, a companion article describes the tools that have been used by the Virgo DetChar group to perform this work.

57 pages, 18 figures. New version, resubmitted to Class. and Quantum Grav. This is the "Results" part of preprint arXiv:2205.01555 [gr-qc] which has been split into two companion articles: one about the tools and methods, the other about the analyses of the O3 Virgo data

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Cited by in corpus (26)

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• Applications of machine learning in gravitational wave research with current interferometric detectors
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• Experimental demonstrations of alignment and mode matching in optical cavities with higher-order Hermite-Gauss modes
• Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data
• Detecting gravitational-wave bursts from black hole binaries in the Galactic Center with LISA
• Neural network time-series classifiers for gravitational-wave searches in single-detector periods
• Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
• Crust (Unified) Tool for Equation-of-state Reconstruction (CUTER) v2
• cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation
• Search for continuous gravitational waves directed at sub-threshold radiometer candidates in O3 LIGO data
• Detection of GW200105 with a targeted eccentric search
• Toward a computationally-efficient follow-up pipeline for blind continuous gravitational-wave searches
• DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
• Scalar fields around black hole binaries in LIGO-Virgo-KAGRA
• Testing modified gravity with the eccentric neutron star--black hole merger GW200105
• Phase consistency test to identify type II strongly lensed gravitational wave signals using a single event
• Effect of kick velocity on gravitational wave detection of binary black holes with space- and ground-based detectors
• Using t-SNE for characterizing glitches in LIGO detectors
• Application of Non-Linear Noise Regression in the Virgo Detector
• Identifying Host Galaxies of Binary Black Hole Mergers with Next-Generation Gravitational Wave Detector Networks
• Multi-scale optimal control for Einstein Telescope active seismic isolation
• Identification of Noise-Associated Glitches in KAGRA O3GK with Hveto
• Searches for Compact Binary Coalescence Events Using Neural Networks in LIGO/Virgo Third Observation Period