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Virgo Detector Characterization and Data Quality: tools
F. Acernese, M. Agathos, A. Ain +491
Detector characterization and data quality studies -- collectively referred to as {\em DetChar} activities in this article -- are paramount to the scientific exploitation of the jo…
Virgo Detector Characterization and Data Quality: results from the O3 run
F. Acernese, M. Agathos, A. Ain +491
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 Adv…
Virgo Detector Characterization and Data Quality during the O3 run
F. Acernese, M. Agathos, A. Ain +491
The Advanced Virgo detector has contributed with its data to the rapid growth of the number of detected gravitational-wave signals in the past few years, alongside the two LIGO ins…
The Virgo O3 run and the impact of the environment
F. Acernese, M. Agathos, A. Ain +486
Sources of geophysical noise (such as wind, sea waves and earthquakes) or of anthropogenic noise impact ground-based gravitational-wave interferometric detectors, causing transient…
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification
Alberto Iess, Elena Cuoco, Filip Morawski +1
We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined…
A guide to LIGO-Virgo detector noise and extraction of transient gravitational-wave signals
The LIGO Scientific Collaboration, the Virgo Collaboration, B. P. Abbott +1135
The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanc…