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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Waveform modeling uncertainties can distort features in the binary black hole mass distribution inferred from gravitational-wave data more than statistical uncertainties.
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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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Is the Binary Black Hole Population Inference from Gravitational-Wave Data Robust?
Waveform modeling uncertainties can distort features in the binary black hole mass distribution inferred from gravitational-wave data more than statistical uncertainties.