Signal processing

Optimus-Primal: A light weight primal-dual solver
A light weight python package for proximal splitting Forward-backward Primal-dual based solver for convex optimisation problems.
Dec 13, 2021
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Optimus-Primal: A light weight primal-dual solver
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, …
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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. …
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Bayesian variational regularisation on the ball
We develop variational regularisation methods which leverage sparsity-promoting priors to solve severely ill posed inverse problems …
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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 …
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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 …
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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 …
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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 …
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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 …
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Sparse Bayesian mass-mapping with uncertainties
Mass-mapping via weak gravitational lensing has until recently lacked principled statistical consideration of uncertainties introduced …
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