Scalable Precision Imaging in Radio Astronomy: from Learned denoisers on GPU to Science (SPIRALS)

The ambitious science goals in radio astronomy for the next decades have triggered the development of a new generation of telescopes targeting imaging the sky with much higher precision (i.e. resolution and sensitivity) than current instruments. Endowing these telescopes with their expected acute vision requires image formation algorithms capable of transforming radio interferometry data into images at target precision, while being robust (i.e. including calibration and uncertainty quantification functionalities), and ultimately scalable to exascale data volumes. The "Scalable Precision Imaging in Radio Astronomy: from Learned denoisers on GPU to Science" (SPIRALS) work programme aims to design transformative deep learning methodology to address this challenge, and apply it on cutting-edge science cases, from the detection of halos and relics in galaxy clusters and detailed morphology mapping of radio galaxies from surveys of the MeerKAT telescope, to black hole imaging with Event Horizon Telescope (EHT) data.

In detail, firstly, artificial neural networks will be trained as simple "denoisers" encapsulating advanced learned physical models of the both radio sky and interfering observation effects. These denoisers will be integrated into a parallel algorithmic structure to define a new image formation algorithm with simultaneous capability for precision, robustness, and scalability. A parallel Python software implementation will be designed for, and mapped onto the latest and most efficient high performance computing hardware technologies, primarily large scale GPU systems. Secondly, as a by-product of the learning of denoisers, a low-cost "post-processor" will be developed to enhance legacy images. Before addressing the science cases, algorithms and software will be validated up to Terabyte image size, using both simulations from the future Square Kilometre Array (SKA) telescope and from the Deep Synoptic Array (DSA-2000) telescope concept, and real data from the Jansky Very Large Array (JVLA) and MeerKAT telescopes, with particular focus on wideband imaging of diffuse emission with complex and faint structure across the field of view.