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CLEANing Cygnus A deep and fast with R2D2

Arwa Dabbech, Amir Aghabiglou, Chung San Chu, Yves Wiaux

TL;DR

This work addresses the long-standing challenge of high-fidelity, fast radio-interferometric imaging by reframing CLEAN within a learned residual-to-residual deep-learning paradigm (R2D2). It introduces three variants—R2D2, R2D2-Net, and R3D3—where a sequence of DNN modules iteratively refines the image by processing back-projected residuals, yielding high-precision reconstructions with minimal major-cycle iterations. The authors demonstrate, for Cygnus A with VLA S-band data, that R2D2 variants achieve imaging quality on par with state-of-the-art optimization-based methods like uSARA and AIRI while significantly reducing reconstruction time, thanks to replacing minor cycles with learned modules. These results establish R2D2 as a fast, robust alternative for high-dynamic-range RI imaging and suggest strong potential for generalization across observing setups and instrument configurations, with future work aimed at robustness, physics-informed training, and scalable image-splitting strategies.

Abstract

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). In this work, we start by shedding light on R2D2's algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a deep neural network (DNN) whose training is iteration-specific. We then proceed with R2D2's first demonstration on real data, for monochromatic intensity imaging of the radio galaxy Cygnus A from S band observations with the Very Large Array (VLA). We show that the modeling power of R2D2's learning approach enables delivering high-precision imaging, superseding the resolution of CLEAN, and matching the precision of modern optimization and plug-and-play algorithms, respectively uSARA and AIRI. Requiring few major-cycle iterations only, R2D2 provides a much faster reconstruction than uSARA and AIRI, known to be highly iterative, and is at least as fast as CLEAN.

CLEANing Cygnus A deep and fast with R2D2

TL;DR

This work addresses the long-standing challenge of high-fidelity, fast radio-interferometric imaging by reframing CLEAN within a learned residual-to-residual deep-learning paradigm (R2D2). It introduces three variants—R2D2, R2D2-Net, and R3D3—where a sequence of DNN modules iteratively refines the image by processing back-projected residuals, yielding high-precision reconstructions with minimal major-cycle iterations. The authors demonstrate, for Cygnus A with VLA S-band data, that R2D2 variants achieve imaging quality on par with state-of-the-art optimization-based methods like uSARA and AIRI while significantly reducing reconstruction time, thanks to replacing minor cycles with learned modules. These results establish R2D2 as a fast, robust alternative for high-dynamic-range RI imaging and suggest strong potential for generalization across observing setups and instrument configurations, with future work aimed at robustness, physics-informed training, and scalable image-splitting strategies.

Abstract

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). In this work, we start by shedding light on R2D2's algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a deep neural network (DNN) whose training is iteration-specific. We then proceed with R2D2's first demonstration on real data, for monochromatic intensity imaging of the radio galaxy Cygnus A from S band observations with the Very Large Array (VLA). We show that the modeling power of R2D2's learning approach enables delivering high-precision imaging, superseding the resolution of CLEAN, and matching the precision of modern optimization and plug-and-play algorithms, respectively uSARA and AIRI. Requiring few major-cycle iterations only, R2D2 provides a much faster reconstruction than uSARA and AIRI, known to be highly iterative, and is at least as fast as CLEAN.
Paper Structure (20 sections, 3 equations, 4 figures, 2 tables)

This paper contains 20 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. RI inverse problem
  3. The R2D2 paradigm
  4. Algorithmic structure
  5. Training procedure
  6. R2D2 variants
  7. When R2D2 meets CLEAN
  8. Application to Cygnus A
  9. Observation and imaging settings
  10. R2D2 incarnations and implementations
  11. Benchmark algorithms
  12. Imaging results
  13. The inner core
  14. The lobes
  15. The hotspots
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Cygnus A: reconstructions of Hö-CLEAN (top) and CS-CLEAN (bottom), displayed in $\textrm{log}_{10}$ scale (negative pixels set to zero for visualization purposes). Their associated residual dirty images are provided in linear scale in panels (a), with standard deviation values $359\times 10^{-4}$, and $8.6\times 10^{-4}$, respectively. Reconstructions are overlaid by zooms on key regions of the radio galaxy: the inner core of Cygnus A in panels (b), the East hotspots in panels (c), and the West hotspots in panels (d), all displayed in $\textrm{log}_{10}$ scale. Reconstructions are available in FITS format in the dataset r2d2cyga.
  • Figure 2: Cygnus A: reconstructions of MS-CLEAN (negative pixels set to zero for visualization purposes; top) and R2D2 (bottom), both displayed in $\textrm{log}_{10}$ scale. Their associated residual dirty images are provided in linear scale in panels (a), with standard deviation values $10.4\times 10^{-4}$, and $11.7\times 10^{-4}$, respectively. Reconstructions are overlaid by zooms on key regions of the radio galaxy: the inner core of Cygnus A in panels (b), the East hotspots in panels (c), and the West hotspots in panels (d), all displayed in $\textrm{log}_{10}$ scale. Reconstructions are available in FITS format in the dataset r2d2cyga.
  • Figure 3: Cygnus A: reconstructions of R2D2-Net (also the first iteration of R3D3; top), and R3D3 (bottom), both displayed in $\textrm{log}_{10}$ scale. Their associated residual dirty images are provided in linear scale in panels (a), with standard deviation values $13.4\times 10^{-4}$, and $9.7\times 10^{-4}$, respectively. Reconstructions are overlaid by zooms on key regions of the radio galaxy: the inner core of Cygnus A in panels (b), the East hotspots in panels (c), and the West hotspots in panels (d), all displayed in $\textrm{log}_{10}$ scale. Reconstructions are available in FITS format in the dataset r2d2cyga.
  • Figure 4: Cygnus A: reconstructions of uSARA (top) and AIRI (bottom), both displayed in $\textrm{log}_{10}$ scale. Their associated residual dirty images are provided in linear scale in panels (a), with standard deviation values $7.2\times 10^{-4}$, and $7.4\times 10^{-4}$, respectively. Reconstructions are overlaid by zooms on key regions of the radio galaxy: the inner core of Cygnus A in panels (b), the East hotspots in panels (c), and the West hotspots in panels (d), all displayed in $\textrm{log}_{10}$ scale. Reconstructions are available in FITS format in the dataset r2d2cyga.