Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.
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Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.
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labrador: A domain-optimized machine-learning tool for gravitational wave inference
Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.
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Mitigating Systematic Errors in Parameter Estimation of Binary Black Hole Mergers in O1-O3 LIGO-Virgo Data
Reanalysis of flagged LVK events with waveform uncertainty models produces consistent spin and precession inferences across raw/deglitched data and multiple waveform approximants.