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miscellaneous-9

Radio Interferometry

3260 papers • 126 benchmarks • 313 datasets

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A practical preconditioner for wide-field continuum imaging of radio interferometric data

H. Bester, A. Repetti, S. Perkins, O. Smirnov, J. Kenyon•Tue Jan 19 2021

The celebrated CLEAN algorithm has been the cornerstone of deconvolution algorithms in radio interferometry almost since its conception in the 1970s. For all its faults, CLEAN is remarkably fast, robust to calibration artefacts and in its ability to model point sources. We demonstrate how the same assumptions that afford CLEAN its speed can be used to accelerate more sophisticated deconvolution algorithms.

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Reconstructing Video from Interferometric Measurements of Time-Varying Sources

W. Freeman, K. Bouman, Adrian V. Dalca, Michael D. Johnson, A. Chael, F. Roelofs, S. Doeleman•Thu Nov 02 2017

A new way to model VLBI measurements is proposed that allows us to recover both the appearance and dynamics of an evolving source by reconstructing a video rather than a static image, and substantially improves results compared to conventional imaging algorithms.

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DeepSource: Point Source Detection using Deep Learning

Zafiirah Hosenie, A. V. Sadr, E. Vos, B. Bassett, N. Oozeer, M. Lochner•Fri Jul 06 2018

The results show that deep learning is a promising approach to point source detection in astronomical images and a key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration.

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Learned radio interferometric imaging for varying visibility coverage

Matthijs Mars, M. Betcke, Jason D. McEwen•Mon May 13 2024

With the next generation of interferometric telescopes, such as the Square Kilometre Array (SKA), the need for highly computationally efficient reconstruction techniques is particularly acute. The challenge in designing learned, data-driven reconstruction techniques for radio interferometry is that they need to be agnostic to the varying visibility coverages of the telescope, since these are different for each observation. Because of this, learned post-processing or learned unrolled iterative reconstruction methods must typically be retrained for each specific observation, amounting to a large computational overhead. In this work we develop learned post-processing and unrolled iterative methods for varying visibility coverages, proposing training strategies to make these methods agnostic to variations in visibility coverage with minimal to no fine-tuning. Learned post-processing techniques are heavily dependent on the prior information encoded in training data and generalise poorly to other visibility coverages. In contrast, unrolled iterative methods, which include the telescope measurement operator inside the network, achieve good reconstruction quality and computation time, generalising well to other coverages and require little to no fine-tuning. Furthermore, they generalise well to more realistic radio observations and are able to reconstruct images with with a larger dynamic range than the training set.

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Self-supervised learning for radio-astronomy source classification: a benchmark

S. Palazzo, C. Spampinato, T. Cecconello, S. Riggi, U. Becciani, F. Vitello, A. Hopkins, G. Vizzari•Wed Nov 20 2024

The results indicate that, SSL-trained models achieve significant improvements over the baseline in several downstream tasks, especially in the linear evaluation setting; when the entire backbone is fine-tuned, the benefits of SSL are less evident but still outperform pretraining.

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Energy and polarization based on-line interference mitigation in radio interferometry

A. Boonstra, S. Yatawatta, C. Broekema•Wed Dec 18 2024

Radio frequency interference (RFI) is a persistent contaminant in terrestrial radio astronomy. While new radio interferometers are becoming operational, novel sources of RFI are also emerging. In order to strengthen the mitigation of RFI in modern radio interferometers, we propose an on-line RFI mitigation scheme that can be run in the correlator of such interferometers. We combine statistics based on the energy as well as the polarization alignment of the correlated signal to develop an on-line RFI mitigation scheme that can be applied to a data stream produced by the correlator in real-time, especially targeted at low duty-cycle or transient RFI detection. In order to improve the computational efficiency, we explore the use of both single precision and half precision floating point operations in implementing the RFI mitigation algorithm. This ideally suits its deployment in accelerator computing devices such as graphics processing units (GPUs) as used by the LOFAR correlator. We provide results based on real data to demonstrate the efficacy of the proposed method.

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IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

No'e Dia, M. J. Yantovski-Barth, Alexandre Adam, Micah Bowles, Laurence Perreault Levasseur, Y. Hezaveh, Anna Scaife•Sat Jan 04 2025

This work introduces Imaging for Radio Interferometry with Score-based models (IRIS), which uses score-based models trained on optical images of galaxies as an expressive prior in combination with a Gaussian likelihood in the uv-space to infer images of protoplanetary disks from visibility data of the DSHARP survey conducted by ALMA.

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Radio Interferometry | State-of-the-Art