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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Dark energy stars from modified Chaplygin gas obey C-I-Λ-f universal relations similar to quark stars but are distinguishable via I-Eg^{-2}, Λ-Eg^{-5}, and f-Eg^{-2} relations, with GW170817 used to predict 1.4 solar-mass properties.
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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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Dark energy stars from the modified Chaplygin gas: $C-I-\Lambda-E_g-f$ universal relations
Dark energy stars from modified Chaplygin gas obey C-I-Λ-f universal relations similar to quark stars but are distinguishable via I-Eg^{-2}, Λ-Eg^{-5}, and f-Eg^{-2} relations, with GW170817 used to predict 1.4 solar-mass properties.