Predictive learn and apply: MAVIS application - learn

Hao Zhang; Jesse Cranney; Nicolas Doucet; Yuxi Hong; Damien Gratadour; Hatem Ltaief; David Keyes; François Rigaut

doi:10.1117/12.2561913

13 December 2020 Predictive learn and apply: MAVIS application - learn

Hao Zhang, Jesse Cranney, Nicolas Doucet, Yuxi Hong, Damien Gratadour, Hatem Ltaief, David Keyes, François Rigaut

Proceedings Volume 11448, Adaptive Optics Systems VII; 114482C (2020) https://doi.org/10.1117/12.2561913
Event: SPIE Astronomical Telescopes + Instrumentation, 2020, Online Only

Conference Poster

Abstract

The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.

Conference Presentation

Citation Download Citation

Hao Zhang, Jesse Cranney, Nicolas Doucet, Yuxi Hong, Damien Gratadour, Hatem Ltaief, David Keyes, and François Rigaut "Predictive learn and apply: MAVIS application - learn", Proc. SPIE 11448, Adaptive Optics Systems VII, 114482C (13 December 2020); https://doi.org/10.1117/12.2561913

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