LIGO detector characterization in the second and third observing runs

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.

Observation of Gravitational Waves from Two Neutron Star–Black Hole Coalescences

“…To verify that instrumental noise artifacts do not bias the analysis of source properties of the observed events, we use data quality validation procedures as in previous events (Abbott et al 2016e;Davis et al 2021), employing sensor arrays at LIGO and Virgo to measure environmental disturbances that could couple into the interferometers (Nguyen et al 2021). In Virgo, we find no evidence of excess power from terrestrial sources for both events.…”

“…After the low-latency identification of GW200115, the extended periods of strain data around the event are further analyzed by the detection pipelines in their offline configurations using improved calibration and refined data quality information that is not available in low latency (Abbott et al 2018b(Abbott et al , 2021bDavis et al 2021). In this analysis, we also subtract nonstationary noise due to the nonlinear coupling of the 60 Hz power mains at the LIGO detectors using coupling functions derived from machine-learning techniques (Vajente et al 2020).…”

Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo’s first three observing runs

“…For the three analyses, we use data from the third observing run (O3) of Advanced LIGO [52] and Advanced Virgo [53]. The detectors took this data between April 1, 2019 andMarch 27, 2020, with a one month pause in data collection in October 2019 and had duty factors of 77%, 75% and 76% for LIGO Livingston (L), LIGO Hanford (H), and Virgo (V), respectively [82].…”

“…In our analysis we use the same time domain cuts that were applied in the O3 isotropic analysis [84] and only analyze data segments during which the detectors were in "observing mode" [82]. We apply a nonstationarity cut to exclude data segments whose power spectral densities vary by more than 20% relative to their neighboring segments.…”

All-sky search for continuous gravitational waves from isolated neutron stars in the early O3 LIGO data

“…To mitigate the effects of these glitches on O3a CW searches for signals below 475 Hz, a simple glitch-gating algorithm was applied [32,33] to excise the transients from the data. For each 1=16-second for which a whitened version of the H1 and L1 strain data channels had excess rms power in the 25-50 Hz or 70-110 Hz bands, the strain channel was set to zero.…”