From the 1 of 6 linked papers with an AI index.
6 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…
Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
Nayyer Raza, Man Leong Chan, Daryl Haggard +5
The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained…
Inferring the spins of merging black holes in the presence of data-quality issues
Rhiannon Udall, Sophie Bini, Katerina Chatziioannou +5
Gravitational waves from black hole binary mergers carry information about the component spins, but inference is sensitive to analysis assumptions, which may be broken by terrestri…
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…
GWSkyNet-Multi II: an updated machine learning model for rapid classification of gravitational-wave events
Nayyer Raza, Man Leong Chan, Daryl Haggard +5
Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formatio…