17 citations · 51 across the 17 of their papers we have counts for
8 papers · 1 filter
Generative imaging for radio interferometry with fast uncertainty quantification
Matthijs Mars, Tobías I. Liaudat, Jessica J. Whitney +2
With the rise of large radio interferometric telescopes, particularly the SKA, there is a growing demand for computationally efficient image reconstruction techniques. Existing rec…
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
Alicja Polanska, Thibeau Wouters, Peter T. H. Pang +2
We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrat…
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…
Learned radio interferometric imaging for varying visibility coverage
Matthijs Mars, Marta M. Betcke, Jason D. McEwen
With the next generation of interferometric telescopes, such as the Square Kilometre Array (SKA), the need for highly computationally efficient reconstruction techniques is particu…
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…