From the 1 of 4 linked papers with an AI index.
4 papers
GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run
Sofia Alvarez-Lopez, Man Leong Chan, Franz S. Herbst +7
The paper presents GSpyNetTree-O4, a machine‑learning tool deployed in the fourth LIGO‑Virgo‑KAGRA observing run to classify detector glitches and validate gravitational‑wave event…
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…