Measuring Close Binary Stars with Speckle Interferometry

Abstract

Speckle interferometry (Labeyrie, 1970) is a well-tested and still used method for detecting and measuring binary stars that are closer together than the width of the atmospheric seeing disk. In this method, an average spatial power spectrum is computed from a series of short-exposure images. The power spectrum exhibits clearly defined cosine fringes, while the autocorrelation function (its Fourier Transform) contains a central peak with two side lobes. Over the years, several methods have been developed to analyze either the autocorrelation function or the power spectrum to determine the separation and orientation of the binary star system (Horch, 1996, Tokovinin, 2010). In this talk, a method for analyzing the fringes in the power spectrum will be described to robustly detect and measure the fringes. The method is based on the detection of the fringe minima and the inflection points between the minima and the maxima. An analysis of the effects of working with the uncalibrated (or poorly calibrated) power spectrum was done. It was found that this method provides a robust detection of the presence of fringes, but that the estimation of the separation of the binary star system has a systematic bias towards larger values. This bias can be overcome with a subsequent inspection of the fringes.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
ADA616793

Entities

People

  • Keith T. Knox

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Air Force Research Laboratories
  • Atmospheric Motion
  • Autocorrelation
  • Binary Stars
  • Detection
  • Diffraction
  • Inspection
  • Interferometry
  • Orientation (Direction)
  • Power Spectra
  • Spearography
  • Spectra
  • Stars
  • Turbulence
  • Two Dimensional
  • Visual Inspection

Fields of Study

  • Physics

Readers

  • Approximation Theory.
  • Astronomy/Astrophysics
  • Image Processing and Computer Vision.