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.
Acerneseet al., Classical and Quantum Gravity32, 024001 (2014)
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Bayesian analysis of GW170817 with PPE framework and EM polarization constraints shows mild preference for scalar mode in quadrupole harmonics and improves bounds on non-GR parameters by up to 60%.
citing papers explorer
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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.
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Tests of scalar polarizations with multi-messenger events
Bayesian analysis of GW170817 with PPE framework and EM polarization constraints shows mild preference for scalar mode in quadrupole harmonics and improves bounds on non-GR parameters by up to 60%.