6 papers · 1 filter
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
David Yallup, Metha Prathaban, James Alvey +4
We present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG)…
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…
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…
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 $Λ…