Copertina

Learned radio interferometric imaging for varying visibility coverage

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mars, Matthijs, Betcke, Marta M., McEwen, Jason D.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Instrumentation and Methods for Astrophysics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
di: Liaudat, Tobías I., et al.
Pubblicazione: (2023)

Learned Interferometric Imaging for the SPIDER Instrument
di: Mars, Matthijs, et al.
Pubblicazione: (2023)

Generative imaging for radio interferometry with fast uncertainty quantification
di: Mars, Matthijs, et al.
Pubblicazione: (2025)

Learned harmonic mean estimation of the marginal likelihood for multimodal posteriors with flow matching
di: Polanska, Alicja, et al.
Pubblicazione: (2026)

Differentiable and accelerated spherical harmonic and Wigner transforms
di: Price, Matthew A., et al.
Pubblicazione: (2023)

Simulation-based inference with scattering representations: scattering is all you need
di: Lin, Kiyam, et al.
Pubblicazione: (2024)

Using conditional GANs for convergence map reconstruction with uncertainties
di: Whitney, Jessica, et al.
Pubblicazione: (2024)

Learned harmonic mean estimation of the marginal likelihood with normalizing flows
di: Polanska, Alicja, et al.
Pubblicazione: (2023)

Differentiable and accelerated wavelet transforms on the sphere and ball
di: Price, Matthew A., et al.
Pubblicazione: (2024)

Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
di: Lin, Kiyam, et al.
Pubblicazione: (2025)

Generative modelling for mass-mapping with fast uncertainty quantification
di: Whitney, Jessica J., et al.
Pubblicazione: (2024)

Lossless compression of simulated radio interferometric visibilities
di: Offringa, A. R., et al.
Pubblicazione: (2025)

The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
di: Piras, Davide, et al.
Pubblicazione: (2024)

Polarization based direction of arrival estimation using a radio interferometric array
di: Yatawatta, Sarod
Pubblicazione: (2025)

Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
di: Price, Matthew A., et al.
Pubblicazione: (2023)

vis-r: Quick radial profile modelling for radio interferometric visibilities
di: Kennedy, Grant M.
Pubblicazione: (2025)

Efficient prior sensitivity analysis for Bayesian model comparison
di: Hu, Zixiao, et al.
Pubblicazione: (2026)

Proximal nested sampling for high-dimensional Bayesian model selection
di: Cai, Xiaohao, et al.
Pubblicazione: (2021)

A visibility-based angular bispectrum estimator for radio-interferometric data
di: Gill, Sukhdeep Singh, et al.
Pubblicazione: (2024)

Africanus III. pfb-imaging -- a flexible radio interferometric imaging suite
di: Bester, Hertzog L., et al.
Pubblicazione: (2024)

Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
di: Polanska, Alicja, et al.
Pubblicazione: (2024)

Self-Supervised Conformal Prediction with Equivariant Bootstrapping for Image Uncertainty Quantification
di: Aldridge, Henry J., et al.
Pubblicazione: (2026)

Evaluating Bayesian deep learning for radio galaxy classification
di: Mohan, Devina, et al.
Pubblicazione: (2024)

Field-level cosmological model selection: field-level simulation-based inference for Stage IV cosmic shear can distinguish dynamical dark energy
di: Mancini, A. Spurio, et al.
Pubblicazione: (2024)

Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
di: Polanska, Alicja, et al.
Pubblicazione: (2024)

Identifying pulsar candidates in interferometric radio images using scintillation
di: Salal, Jitendra, et al.
Pubblicazione: (2024)

Classification of compact radio sources in the Galactic plane with supervised machine learning
di: Riggi, S., et al.
Pubblicazione: (2024)

RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images
di: Hossain, M. S., et al.
Pubblicazione: (2025)

That pesky A-term: Efficiently correcting for direction-, time-, and baseline-dependent effects in radio interferometric imaging
di: Hodgson, Torrance
Pubblicazione: (2025)

A distributed resource-adaptive implementation of the widefield radio-interferometric measurement model for scalable image formation
di: Dabbech, Arwa, et al.
Pubblicazione: (2026)

A multilevel framework for accelerating uSARA in radio-interferometric imaging
di: Lauga, Guillaume, et al.
Pubblicazione: (2024)

Generative models of astrophysical fields with scattering transforms on the sphere
di: Mousset, Louise, et al.
Pubblicazione: (2024)

Deep radio interferometric search for decametre radio emission from the exoplanet Tau Boötis b
di: Cordun, C. M., et al.
Pubblicazione: (2025)

Closure invariants for polarised radio interferometric observations: a graph theoretical approach
di: Kumar, Vinay, et al.
Pubblicazione: (2024)

Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry
di: Cherif, Mostafa, et al.
Pubblicazione: (2024)

Million Points of Light (MPoL): a PyTorch library for radio interferometric imaging and inference
di: Czekala, Ian, et al.
Pubblicazione: (2025)

Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays
di: Ferrière, Arsène, et al.
Pubblicazione: (2026)

Near-field imaging of local interference in radio interferometric data: Impact on the redshifted 21 cm power spectrum
di: Munshi, S., et al.
Pubblicazione: (2025)

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
di: Abduallah, Yasser, et al.
Pubblicazione: (2024)

Making the unmodulated pyramid wavefront sensor smart II. First on-sky demonstration of extreme adaptive optics with deep learning
di: Landman, R., et al.
Pubblicazione: (2025)