GPU-accelerated nested sampling on GW170817 demonstrates that switching to a uniform-in-dL prior shifts the H0 tail and median far more than post-hoc reweighting captures, due to an under-sampled (dL, iota) bimodality.
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A simulation-based inference method with Gaussian process emulators trained on 1300 kilonova simulations recovers parameters accurately and rapidly while avoiding MCMC biases from likelihood misspecification.
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Rapid Hubble constant inference from GW170817 using GPU-accelerated nested sampling: prior sensitivity and the limits of post-hoc reweighting
GPU-accelerated nested sampling on GW170817 demonstrates that switching to a uniform-in-dL prior shifts the H0 tail and median far more than post-hoc reweighting captures, due to an under-sampled (dL, iota) bimodality.
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Rapid and robust simulation-based inference for kilonovae
A simulation-based inference method with Gaussian process emulators trained on 1300 kilonova simulations recovers parameters accurately and rapidly while avoiding MCMC biases from likelihood misspecification.