5 papers
Reduction of transient noise artifacts in gravitational-wave data using deep learning
Kentaro Mogushi
Excess transient noise artifacts, or glitches impact the data quality of ground-based gravitational-wave (GW) detectors and impair the detection of signals produced by astrophysica…
Application of a new transient-noise analysis tool for an unmodeled gravitational-wave search pipeline
Kentaro Mogushi
Excess transient noise events, or glitches, impact the data quality of ground-based ravitational-wave (GW) detectors and impair the detection of signals produced by astrophysical s…
NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals
Kentaro Mogushi, Ryan Quitzow-James, Marco Cavaglià +2
Instrumental and environmental transient noise bursts in gravitational-wave detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localizatio…
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…
Improving astrophysical parameter estimation via offline noise subtraction for Advanced LIGO
J. C. Driggers, S. Vitale, A. P. Lundgren +228
The Advanced LIGO detectors have recently completed their second observation run successfully. The run lasted for approximately 10 months and lead to multiple new discoveries. The…