Wavefront reconstruction by machine learning using the delta rule

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsMark A. Ealey, Fritz Merkle
Pages629-635
Number of pages7
StatePublished - Dec 1 1994
EventAdaptive Optics in Astronomy - Kailua, HI, USA
Duration: Mar 17 1994Mar 18 1994

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume2201
ISSN (Print)0277-786X

Other

OtherAdaptive Optics in Astronomy
CityKailua, HI, USA
Period3/17/943/18/94

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Angel, J. R. P. (1994). Wavefront reconstruction by machine learning using the delta rule. In M. A. Ealey, & F. Merkle (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (pp. 629-635). (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 2201).