The Virgo O3 run and the impact of the environment

Abstract

Sources of geophysical noise (such as wind, sea waves and earthquakes) or of anthropogenic noise impact ground-based gravitational-wave interferometric detectors, causing transient sensitivity worsening and gaps in data taking. During the one year-long third Observing Run (O3: from April 01, 2019 to March 27, 2020), the Virgo Collaboration collected a statistically significant dataset, used in this article to study the response of the detector to a variety of environmental conditions. We correlated environmental parameters to global detector performance, such as observation range, duty cycle and control losses. Where possible, we identified weaknesses in the detector that will be used to elaborate strategies in order to improve Virgo robustness against external disturbances for the next data taking period, O4, currently planned to start at the end of 2022. The lessons learned could also provide useful insights for the design of the next generation of ground-based interferometers.

49 pages, 27 figures. Published in Classical and Quantum Grav

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Cited by in corpus (10)

• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• Science with the Einstein Telescope: a comparison of different designs
• Virgo Detector Characterization and Data Quality: results from the O3 run
• Correlated 1-1000 Hz magnetic field fluctuations from lightning over earth-scale distances and their impact on gravitational wave searches
• Virgo Detector Characterization and Data Quality: tools
• First 3D reconstruction of a blast furnace using muography
• Identifying glitches near gravitational-wave signals from compact binary coalescences using the Q-transform
• A Unified $p_{\mathrm{astro}}$ for Gravitational Waves: Consistently Combining Information from Multiple Search Pipelines
• The impact of local noise recorded at the ET candidate sites on the signal to noise ratio of CBC gravitational wave signals for the ET triangle configuration
• Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors