Multi time-step wavefront reconstruction for tomographic adaptive-optics systems

Yoshito H. Ono, Masayuki Akiyama, Shin Oya, Olivier Lardiére, David R. Andersen, Carlos Correia, Kate Jackson, Colin Bradley

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

In tomographic adaptive-optics (AO) systems, errors due to tomographic wavefront reconstruction limit the performance and angular size of the scientific field of view (FoV), where AO correction is effective. We propose a multi time-step tomographic wavefront reconstruction method to reduce the tomographic error by using measurements from both the current and previous time steps simultaneously. We further outline the method to feed the reconstructor with both wind speed and direction of each turbulence layer. An end-to-end numerical simulation, assuming a multi-object AO (MOAO) system on a 30 m aperture telescope, shows that the multi timestep reconstruction increases the Strehl ratio (SR) over a scientific FoV of 10 arc min in diameter by a factor of 1.5-1.8 when compared to the classical tomographic reconstructor, depending on the guide star asterism and with perfect knowledge of wind speeds and directions. We also evaluate the multi time-step reconstruction method and the wind estimation method on the RAVEN demonstrator under laboratory setting conditions. The wind speeds and directions at multiple atmospheric layers are measured successfully in the laboratory experiment by our wind estimation method with errors below 2 ms-1. With these wind estimates, the multi time-step reconstructor increases the SR value by a factor of 1.2-1.5, which is consistent with a prediction from the end-to-end numerical simulation.

Original languageEnglish
Pages (from-to)726-740
Number of pages15
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume33
Issue number4
DOIs
Publication statusPublished - 2016 Apr

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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