From the 1 of 10 linked papers with an AI index.
10 papers
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +4
The paper introduces a GPU‑optimized nested sampling algorithm that can perform rapid, full‑waveform gravitational‑wave parameter estimation for binary neutron star events, achievi…
Rapid Hubble constant inference from GW170817 using GPU-accelerated nested sampling: prior sensitivity and the limits of post-hoc reweighting
Ming Han Yang, Metha Prathaban, David Yallup +1
The bright-siren measurement of the Hubble constant from GW170817 (Abbott et al. 2017) assumes that switching from a volumetric to a uniform-in- luminosity-distance prior can…
The Bayesian view of DESI DR2 with unimpeded: Evidence and tension in a combined analysis with CMB and supernovae across cosmological models
Dily Duan Yi Ong, David Yallup, Will Handley
We apply the framework to perform a fully Bayesian reanalysis of the DESI DR2 data, using nested sampling with to compute evidences for $Î…
jaxsgp4: GPU-accelerated mega-constellation propagation with batch parallelism
Charlotte Priestley, Will Handley
As the population of anthropogenic space objects transitions from sparse clusters to mega-constellations exceeding 100,000 satellites, traditional orbital propagation techniques fa…
Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation
Toby Lovick, David Yallup, Will Handley
We present Automatic Laplace Collapsed Sampling (ALCS), a general framework for marginalising latent parameters in Bayesian models using automatic differentiation, which we combine…
Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
S. A. K. Leeney, T. Gessey-Jones, W. J. Handley +3
For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Est…