Wavefront reconstruction by machine learning using the delta rule

James Roger P. Angel

doi:10.1117/12.176098

31 May 1994 Wavefront reconstruction by machine learning using the delta rule

James Roger P. Angel

Proceedings Volume 2201, Adaptive Optics in Astronomy; (1994) https://doi.org/10.1117/12.176098
Event: 1994 Symposium on Astronomical Telescopes and Instrumentation for the 21st Century, 1994, Kailua, Kona, HI, United States

Abstract

In this paper we use phase screen models to illustrate the power of the delta rule, by obtaining the optimum reconstructor for a Shack-Hartmann sensor with just 6 subapertures in the form of pie segments. The dependence of the matrix elements and residual error on measurement noise is determined, and the accuracy compared with theoretical limits. Reconstructors for more complex problems involving time dependence and multiple laser spots are ideal applications for the method.

Citation Download Citation

James Roger P. Angel "Wavefront reconstruction by machine learning using the delta rule", Proc. SPIE 2201, Adaptive Optics in Astronomy, (31 May 1994); https://doi.org/10.1117/12.176098

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