Predictive learn and apply: MAVIS application - apply

The Learn and Apply tomographic reconstructor coupled with the pseudo open-loop control scheme shows promising results in simulation for multi-conjugate adaptive optics systems. We motivate, derive, and demonstrate the inclusion of a predictive step in the Learn and Apply tomographic reconstructor based on frozen-flow turbulence assumption. The addition of this predictive step provides an additional gain in performance, especially at larger wave-front sensor exposure periods, with no increase of online computational burden. We provide results using end-to-end numerical simulations for a multi-conjugate adaptive optics system for an 8m telescope based on the MAVIS system design.

[1]  Damien Gratadour,et al.  COMPASS: An Efficient GPU-based Simulation Software for Adaptive Optics Systems , 2018, 2018 International Conference on High Performance Computing & Simulation (HPCS).

[2]  Luc Gilles,et al.  Robustness study of the pseudo open-loop controller for multiconjugate adaptive optics. , 2005, Applied optics.

[3]  G. Rousset,et al.  Tomography approach for multi-object adaptive optics. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Ruth C. Carter,et al.  Principles , 2003, Law’s Reality.

[5]  Donald T. Gavel,et al.  Toward Strehl-optimizing adaptive optics controllers , 2003, SPIE Astronomical Telescopes + Instrumentation.

[6]  M J Townson,et al.  AOtools: a Python package for adaptive optics modelling and analysis. , 2019, Optics express.

[7]  Francois Rigaut,et al.  Principles, limitations, and performance of multiconjugate adaptive optics , 2000, Astronomical Telescopes and Instrumentation.

[8]  Francois Rigaut,et al.  Stochastic Levenberg-Marquardt for Solving Optimization Problems on Hardware Accelerators , 2020 .

[9]  A. Sevin,et al.  Real-time end-to-end AO simulations at ELT scale on multiple GPUs with the COMPASS platform , 2018, Astronomical Telescopes + Instrumentation.

[10]  Nicolas Doucet,et al.  Predictive learn and apply: MAVIS application - learn , 2020, Astronomical Telescopes + Instrumentation.

[11]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[12]  A. Sevin,et al.  A novel fast and accurate pseudo-analytical simulation approach for MOAO , 2014, Astronomical Telescopes and Instrumentation.

[13]  Brent L. Ellerbroek,et al.  Simulations of closed-loop wavefront reconstruction for multiconjugate adaptive optics on giant telescopes , 2003, SPIE Optics + Photonics.

[14]  N. Hubin,et al.  Optimized modal tomography in adaptive optics , 2001 .

[15]  Jean-Pierre Véran,et al.  Spatio-angular minimum-variance tomographic controller for multi-object adaptive-optics systems. , 2015, Applied optics.

[16]  C. Kulcsár,et al.  Optimal control law for classical and multiconjugate adaptive optics. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Lisa Poyneer,et al.  Experimental verification of the frozen flow atmospheric turbulence assumption with use of astronomical adaptive optics telemetry. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Francois Rigaut,et al.  MAVIS: system modelling and performance prediction , 2020, Astronomical Telescopes + Instrumentation.

[19]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[20]  C Bradley,et al.  Static and predictive tomographic reconstruction for wide-field multi-object adaptive optics systems. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.