5 papers
Towards end-to-end Bayesian forward models in global 21-cm cosmology: surrogate modelling and marginalisation of beam uncertainty
Jacob L. Tutt, Dominic J. Anstey, John Cumner +3
Robust statistical inference in global 21-cm cosmology requires end-to-end uncertainty quantification that jointly handles the highly degenerate cosmological signal, foreground emi…
Scalable Bayesian data curation for next-generation radio experiments
S. A. K. Leeney, E. de Lera Acedo, W. J. Handley +42
Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automati…
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…
Optimizing Foreground Modelling for Global 21cm Cosmology with GPU-Accelerated Nested Sampling
Jacob L. Tutt, Peter H. Sims, Joe H. N. Pattison +3
The global 21-cm signal provides a powerful probe of early-Universe astrophysics, but its detection is hindered by Galactic foregrounds that are orders of magnitude brighter than t…
JAXMg: A multi-GPU linear solver in JAX
Jacob Tutt, Roeland Wiersema
Solving large dense linear systems and eigenvalue problems is a core requirement in many areas of scientific computing, but scaling these operations beyond a single GPU remains cha…