Publications

Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. …
PDF Cite Code arXiv
Bayesian model comparison for simulation-based inference
Comparison of appropriate models to describe observational data is a fundamental task of science. The Bayesian model evidence, or …
PDF Cite Code arXiv
Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator
We resurrect the infamous harmonic mean estimator for computing the marginal likelihood (Bayesian evidence) and solve its problematic …
PDF Cite Code arXiv
Mapping dark matter on the celestial sphere with weak gravitational lensing
Convergence maps of the integrated matter distribution are a key science result from weak gravitational lensing surveys. To date, …
PDF Cite Code DOI arXiv
Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. …
PDF Cite DOI arXiv
Bayesian variational regularisation on the ball
We develop variational regularisation methods which leverage sparsity-promoting priors to solve severely ill posed inverse problems …
PDF Cite arXiv
Sparse image reconstruction on the sphere: a general approach with uncertainty quantification
Inverse problems defined naturally on the sphere are becoming increasingly of interest. In this article we provide a general framework …
PDF Cite arXiv
Sparse Bayesian mass-mapping with uncertainties: Full sky observations on the celestial sphere
To date weak gravitational lensing surveys have typically been restricted to small fields of view, such that the flat-sky approximation …
PDF Cite DOI arXiv
Efficient generalized spherical CNNs
Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be …
PDF Cite Code arXiv
Scale-discretised ridgelet transform on the sphere
We revisit the spherical Radon transform, also called the Funk-Radon transform, viewing it as an axisymmetric convolution on the …
PDF Cite Code DOI arXiv
Sparse Bayesian mass-mapping with uncertainties: local credible intervals
Until recently, mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical …
PDF Cite DOI arXiv
Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations
Weak lensing convergence maps – upon which higher order statistics can be calculated – can be recovered from observations of the shear …
PDF Cite DOI arXiv
Sparse Bayesian mass-mapping with uncertainties
Mass-mapping via weak gravitational lensing has until recently lacked principled statistical consideration of uncertainties introduced …
PDF Cite arXiv