CLEANing Cygnus A deep and fast with R2D2
This work addresses high-precision and fast imaging in radio astronomy, offering a novel deep learning approach that improves upon traditional and modern methods, though it is incremental as it builds on existing R2D2 and CLEAN frameworks.
The authors tackled radio interferometry imaging of the Cygnus A galaxy by applying the R2D2 deep learning method, achieving higher resolution than CLEAN, matching the precision of modern algorithms like uSARA and AIRI, and providing faster reconstruction than these iterative methods while being as fast as CLEAN.
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.