SORS: Harnessing Machine Learning and High Performance Computing in Adaptive Optics for High-Contrast Imaging in the Subaru Telescope
As part of the Barelona Supercomputing Center's Severo Ochoa Research Seminar Series, Bartomeu Pou presented on, "Harnessing Machine Learning and High Performance Computing in Adaptive Optics for High-Contrast Imaging in the Subaru Telescope". His presenntation focused on the new generation of ground-based optical telescopes, classified as extremely large telescopes, These >=30 m diameter telescopes hold the potential to directly image rocky exoplanets in the habitable zone of extrasolar systems only if certain technological challenges are met. A crucial challenge is the real-time correction of the atmospheric distortions on the image, handled at frequencies of 1-2 KHz, a task undertaken by Adaptive Optics (AO) systems.
In his presentation he explores the implementation and enhanced performance of different machine learning (ML) techniques to improve the performance of the AO system, as part of the Subaru Coronagraphic extreme Adaptive Optics (SCExAO) team for the 8.2m Subaru telescope in Hawaii. His approach consists of a pipeline that combines supervised learning and reinforcement learning with offline training and online fine-tuning to adapt to changing atmospheric conditions. To manage the loop frequency, he leverages high performance computing techniques, implementing an ML extension of the Modular Image processing Library toolKit (MILK) package, which enables efficient shared memory communication between processes.
