17 citations · 51 across the 17 of their papers we have counts for
11 papers · 1 filter
LoFi: Neural Local Fields for Scalable Image Reconstruction
AmirEhsan Khorashadizadeh, Tobías I. Liaudat, Tianlin Liu +2
Neural fields or implicit neural representations (INRs) have attracted significant attention in computer vision and imaging due to their efficient coordinate-based representation o…
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…
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 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…
A covariant formulation for cosmological radiative transfer of the 21-cm line
Jennifer Y. H. Chan, Qin Han, Kinwah Wu +1
The 21-cm hyperfine line of neutral hydrogen is a useful tool to probe the conditions of the Universe during the Dark Ages, Cosmic Dawn, and the Epoch of Reionisation. In most of t…
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…