Radio Interferometry
3 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Bayesian Imaging for Radio Interferometry with Score-Based Priors
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
CLEANing Cygnus A deep and fast with R2D2
A novel deep learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2).
Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions
Nous d\'emontrons sa sup\'eriorit\'e par rapport aux m\'ethodes proximales dans des situations en grande dimension avec des mesures de Fourier, lors de la r\'esolution de probl\`emes simul\'es inspir\'es de la radio-interf\'erom\'etrie.
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm.
VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks
Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole.
Wavefront sensor for millimeter/submillimeter-wave adaptive optics based on aperture-plane interferometry
We present a concept of a millimeter wavefront sensor that allows real-time sensing of the surface of a ground-based millimeter/submillimeter telescope.
Statistical Performance of Radio Interferometric Calibration
In this paper, we study the statistical performance of direction dependent distributed calibration, i. e., the distortion caused by calibration on the residual statistics.
Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before.
A sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing
An algorithmic framework to compute sparse or minimal-TV solutions of linear systems is proposed.
