Machine Learning and Adaptive Optics | Jesse Granney and Charles Gretton

Speaker: Jesse Granney (Postdoctoral Fellow at Australian National University College of Science) and Charles Gretton (TechLauncher Program Convener, at Australian National University College of Engineering, Computing and Cybernetics)

Atmospheric turbulence severely limits the quality of science able to be retrieved using ground-based astronomical telescopes. As these telescopes continue to grow larger, this effect becomes more pronounced, demanding the use of real-time “adaptive optics”. The current era of adaptive optics comes with unique data challenges, including the processing of tens of thousands of noisy measurements to compute thousands of commands every, all within a couple of milliseconds. Traditionally, this real-time demand would only allow linear control laws to be employed, but the massive parallelisation allowed by CNN-based solutions is beginning to attract attention in the adaptive optics landscape as a non-linear alternative. CNNs (namely, the CGAN and UNet) promise a significant improvement to the quality of science achievable in astronomical instruments.
Charles and Jesse give a gentle introduction to the general problem statement in the context of adaptive optics, and an overview of their work in this domain.

This talk is part of the Liverpool Virtual Seminar Series on Data Intensive Science; more information can be found at https://indico.ph.liv.ac.uk/e/data_sc...

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