28 citations · 163 across the 23 of their papers we have counts for
23 papers
Generative modelling for mass-mapping with fast uncertainty quantification
Jessica J. Whitney, Tobías I. Liaudat, Matthew A. Price +2
Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-…
Scattering transforms on the sphere, application to large scale structure modelling
Louise Mousset, Erwan Allys, Matthew A. Price +4
Scattering transforms are a new type of summary statistics recently developed for the study of highly non-Gaussian processes, which have been shown to be very promising for astroph…
Generative models of astrophysical fields with scattering transforms on the sphere
Louise Mousset, Erwan Allys, Matthew A. Price +4
Scattering transforms are a new type of summary statistics recently developed for the study of highly non-Gaussian processes, which have been shown to be very promising for astroph…
Using conditional GANs for convergence map reconstruction with uncertainties
Jessica Whitney, Tobías Liaudat, Matt Price +2
Understanding the large-scale structure of the Universe and unravelling the mysteries of dark matter are fundamental challenges in contemporary cosmology. Reconstruction of the cos…
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
Davide Piras, Alicja Polanska, Alessio Spurio Mancini +2
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian…
Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
Alicja Polanska, Matthew A. Price, Davide Piras +2
We present the learned harmonic mean estimator with normalizing flows - a robust, scalable and flexible estimator of the Bayesian evidence for model comparison. Since the estimator…
Differentiable and accelerated wavelet transforms on the sphere and ball
Matthew A. Price, Alicja Polanska, Jessica Whitney +1
Directional wavelet dictionaries are hierarchical representations which efficiently capture and segment information across scale, location and orientation. Such representations dem…
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
Tobías I. Liaudat, Matthijs Mars, Matthew A. Price +3
Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensi…
Differentiable and accelerated spherical harmonic and Wigner transforms
Matthew A. Price, Jason D. McEwen
Many areas of science and engineering encounter data defined on spherical manifolds. Modelling and analysis of spherical data often necessitates spherical harmonic transforms, at h…
Proximal nested sampling with data-driven priors for physical scientists
Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price +2
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for mode…
Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
Matthew A. Price, Matthijs Mars, Matthew M. Docherty +3
Cosmic strings are linear topological defects that may have been produced during symmetry-breaking phase transitions in the very early Universe. In an expanding Universe the existe…
Learned harmonic mean estimation of the marginal likelihood with normalizing flows
Alicja Polanska, Matthew A. Price, Alessio Spurio Mancini +1
Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare mod…
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
Jeremy Ocampo, Matthew A. Price, Jason D. McEwen
No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivaria…
Bayesian model comparison for simulation-based inference
A. Spurio Mancini, M. M. Docherty, M. A. Price +1
Comparison of appropriate models to describe observational data is a fundamental task of science. The Bayesian model evidence, or marginal likelihood, is a computationally challeng…
Machine learning assisted Bayesian model comparison: learnt harmonic mean estimator
Jason D. McEwen, Christopher G. R. Wallis, Matthew A. Price +1
We resurrect the infamous harmonic mean estimator for computing the marginal likelihood (Bayesian evidence) and solve its problematic large variance. The marginal likelihood is a k…
Bayesian variational regularization on the ball
Matthew A. Price, Jason D. McEwen
We develop variational regularization methods which leverage sparsity-promoting priors to solve severely ill posed inverse problems defined on the 3D ball (i.e. the solid sphere).…
Sparse image reconstruction on the sphere: a general approach with uncertainty quantification
Matthew A. Price, Luke Pratley, Jason D. McEwen
Inverse problems defined naturally on the sphere are becoming increasingly of interest. In this article we provide a general framework for evaluation of inverse problems on the sph…
Efficient Generalized Spherical CNNs
Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker +4
Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance…
Spherical Bayesian mass-mapping with uncertainties: full sky observations on the celestial sphere
Matthew A. Price, Jason D. McEwen, L. Pratley +1
To date weak gravitational lensing surveys have typically been restricted to small fields of view, such that the has been sufficiently satisfied.…
Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +1
Weak lensing convergence maps - upon which higher order statistics can be calculated - can be recovered from observations of the shear field by solving the lensing inverse problem.…
Sparse Bayesian mass-mapping with uncertainties: local credible intervals
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +2
Until recently mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstructio…
Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure
Matthew A. Price, Jason D. McEwen, Xiaohao Cai +2
A crucial aspect of mass-mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been…
Mapping dark matter on the celestial sphere with weak gravitational lensing
Christopher G. R. Wallis, Matthew A. Price, Jason D. McEwen +3
Convergence maps of the integrated matter distribution are a key science result from weak gravitational lensing surveys. To date, recovering convergence maps has been performed usi…