The first informative astrophysical calibration of gravitational-wave detectors is reported using GW240925 and GW250207.
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representative citing papers
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
VIGILant applies tree-based models and a ResNet CNN to classify Virgo O3b glitches with 98% accuracy and has been deployed for daily use with an interactive dashboard.
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
Residuals after subtracting best-fit waveforms from GWTC-3 events show no significant deviation from noise according to three standard goodness-of-fit tests.
citing papers explorer
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GW240925 and GW250207: Astrophysical Calibration of Gravitational-wave Detectors
The first informative astrophysical calibration of gravitational-wave detectors is reported using GW240925 and GW250207.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Evidence for eccentricity in the population of binary black holes observed by LIGO-Virgo-KAGRA
Bayesian inference on LVK O1-O3 events with eccentric aligned-spin waveforms yields log10 Bayes factors of 1.77-4.75 favoring eccentricity for GW200129, GW190701 and GW200208_22, and >99.5% probability that at least one of 57 events is eccentric under an astrophysically motivated rate prior.
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Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
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VIGILant: an automatic classification pipeline for glitches in the Virgo detector
VIGILant applies tree-based models and a ResNet CNN to classify Virgo O3b glitches with 98% accuracy and has been deployed for daily use with an interactive dashboard.
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Hunting for new glitches in LIGO data using community science
Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.
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Residual Test for the Third Gravitational-Wave Transient Catalog
Residuals after subtracting best-fit waveforms from GWTC-3 events show no significant deviation from noise according to three standard goodness-of-fit tests.