Design of an Optimized Supervisor Module for Tomographic Adaptive Optics Systems of Extremely Large Telescopes

  • Nicolas Doucet

Student thesis: Doctoral Thesis


The recent advent of next generation ground-based telescopes, code-named Extremely Large Telescopes (ELT), highlights the beginning of a forced march toward an era of deploying instruments capable of exploiting starlight captured by mirrors at an unprecedented scale. This confronts the astronomy community with both a daunting challenge and a unique opportunity. The challenge arises from the mismatch between the complexity of current instruments and their expected scaling with the square of the future telescope diameters, on which astronomy applications have relied to produce better science. To deliver on the promise of tomorrow’s ELT, astronomers must design new technologies that can effectively enhance the performance of the instrument at scale, while compensating for the atmospheric turbulence in real-time. This is an unsolved problem. This problem presents an opportunity because the astronomy community is now compelled to rethink essential components of the optical systems and their traditional hardware/software ecosystems in order to achieve high optical performance with a near real-time computational response. In order to realize the full potential of such instruments, we investigate a technique supporting Adaptive Optics (AO), i.e., a dedicated concept relying on turbulence tomography. In particular, a critical part of AO systems is the supervisor module, which is responsible for providing the system with a Tomographic Reconstructor (ToR) at a regular pace, as the atmospheric turbulence evolves over an observation window. In this thesis, we implement an optimized supervisor module and assess it under real configurations of the future European ELT (E-ELT) with a 39m diameter, the largest and most complex optical telescope ever conceived. This necessitates manipulating large matrix sizes (i.e., up to 100k × 100k) that contain measurements captured by multiple wavefront sensors. To address the complexity bottleneck, we employ high performance computing software solutions based on cutting-edge numerical algorithms using asynchronous, fine-grained computations as well as approximations techniques that leverage the resulting matrix data structure. Furthermore, GPU-based hardware accelerators are used in conjunction with the software solutions to ensure reasonable time-to-solution to cope with rapidly evolving atmospheric turbulence. The proposed software/hardware solution permits to reconstruct an image with high accuracy. We demonstrate the validity of the AO systems with a third-party testbed simulating at the E-ELT scale, which is intended to pave the way for a first prototype installed on-site
Date of AwardJan 8 2020
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Science and Engineering
SupervisorDavid Keyes (Supervisor)


  • Adaptive Optics
  • High Performance Computing
  • Real Time Controller
  • Extremely Large Telescope

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