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.
Aasiet al., Classical and Quantum Gravity32, 074001 (2015)
2 Pith papers cite this work. Polarity classification is still indexing.
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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%.
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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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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%.