415 citations · 1.6k across the 22 of their papers we have counts for
12 papers · 1 filter
Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
Derek Davis, Zach Yarbrough, Joseph Areeda +30
We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth…
GSpyNetTreeS: a machine learning solution for glitch localization in time and frequency
Man Leong Chan, Jess McIver, Yannick Lecoeuche +6
Data from ground-based gravitational wave detectors are often contaminated by non-Gaussian instrumental artifacts or detector noise transients. Unbiased source property estimation…
LIGO Detector Characterization in the first half of the fourth Observing run
S. Soni, B. K. Berger, D. Davis +232
Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, de…
GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts
Man Leong Chan, Jess McIver, Ashish Mahabal +12
Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients.…
Characterization of systematic error in Advanced LIGO calibration in the second half of O3
Ling Sun, Evan Goetz, Jeffrey S. Kissel +19
We present the probability distribution of the systematic errors in the most accurate, high-latency version of the reconstructed dimensionless strain , at the Hanford and Living…
LIGO Detector Characterization in the Second and Third Observing Runs
D. Davis, J. S. Areeda, B. K. Berger +284
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 detecta…