Learning to control a primary mirror

Reinforcement Learning for Estimation and Control in Terrestrial Telescopes.

Picture of charles-gretton.md Charles Gretton

12 Jun 2023

Supervisors:

- Jesse Cranney (Research School of Astronomy and Astrophysics),

- Charles Gretton (School of Computing)

The Giant Magellan Telescope (GMT) requires real-time phasing of its primary mirror segments. Each instrument of GMT is required to provide phasing telemetry from guide-star images. The required phasing data is embedded in these images in a highly non-linear way. Neural-network based solutions ought to be capable of extracting this data efficiently, and in a way that is tuned online to perform optimally under time-varying conditions.

Learning outcomes for a student undertaking this project include:

  1. Proficiency in real-time data processing and analysis.
  2. Skill development in utilizing neural network-based solutions for efficient data extraction.
  3. Understanding of complex data structures and their analysis.
  4. Application of adaptive techniques for optimal performance in dynamic conditions.

Competency in Python programming is required. Prior experience in control and estimation  theory, Machine Learning, and Fourier Optics is valued.

Background: We have been collaborating prototyping new network-based workflows for astronomy instrumentation. For some background reading, checkout our recent papers:

Smith et al., “Enhanced Adaptive Optics Control with Image to Image Translation”, UAI 2022.

Smith et al., “ Image-to-image translation for wavefront and point spread function estimation “, Journal of Astronomical Telescopes, Instruments, and Systems, Vol. 9, Issue 1, 019001 (January 2023). https://doi.org/10.1117/1.JATIS.9.1.019001

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