CLEANing Cygnus A deep and fast with R2D2

• URL: http://arxiv.org/abs/2309.03291v3
• Date: Tue, 23 Apr 2024 17:32:37 GMT
• Title: CLEANing Cygnus A deep and fast with R2D2
• Authors: Arwa Dabbech, Amir Aghabiglou, Chung San Chu, Yves Wiaux,
• Abstract summary: 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) We show that 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.
• Score: 1.7249361224827533
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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