Abstract
The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.
Highlights
The Laser Interferometer Gravitational-wave Observatory (LIGO) [1] and Virgo [2] are the most sensitive facilities for the direct detection of gravitational-waves (GWs)
We report the results of detector characterization methods applied to LIGO detector data from O2 and O3 to improve the performance of the detectors and astrophysical analyses
There is a marked improvement in the stability of the LIGO detectors between O2 and O3, with coincident science quality time increasing by some 16%
Summary
The Laser Interferometer Gravitational-wave Observatory (LIGO) [1] and Virgo [2] are the most sensitive facilities for the direct detection of gravitational-waves (GWs). In order to address these features of the data that differ from the output of an idealized gravitational-wave interferometer, the LIGO detectors and data are closely monitored before and during observing runs using a large number of additional data streams (referred to as auxiliary channels), that include sensors of the environment surrounding the detectors and measurements of the detector control systems. These efforts to understand and mitigate these sources of noise, both in the instrument and the data are collectively referred to as “detector characterization”.
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