Towards learned exascale computational imaging for the SKA

27 Sept 2024

Speaker: Jason McEwen (UCL)

Abstract:

Exascale computational challenges in astrophysics range across big-data, big-models, and big-sims, all of which require big-compute. In this talk I will focus on the big-data challenge of the Square Kilometre Array (SKA), the next-generation of radio interferometric telescopes, which is currently under construction. The SKA will deliver unprecedented resolution and sensitivity that will unlock numerous science goals, ranging from studying dark matter and dark energy, to extreme tests of general relativity, to observing for the first time the epoch when luminous objects in the Universe formed. However, the SKA also presents unprecedented data processing challenges and is a truly exascale experiment. Imaging raw observations of the SKA requires solving an ill-posed inverse problem that has been identified as a critical bottleneck in current data processing pipelines. I will review highly distributed and parallelised algorithms to scale computational inverse imaging to the exascale. Furthermore, I will describe how artificial intelligence (AI) can be integrated into this approach to realise a hybrid physics-AI approach that can leverage big-sims and, perhaps surprisingly, small-models, providing superior reconstruction quality, further acceleration, and uncertainty quantification.