UCAC3 Pixel Processing

Abstract

The third US Naval Observatory CCD Astrograph Catalog, UCAC3, was released at the IAU General Assembly on 2009 August 10. It is a highly accurate, all-sky astrometric catalog of about 100 million stars in the R = 8-16 mag range. Recent epoch observations are based on over 270,000 CCD exposures, which have been reprocessed for the UCAC3 release applying traditional and new techniques. Challenges in the data have been high dark current and asymmetric image profiles due to the poor charge transfer efficiency of the detector. Non-Gaussian image profile functions were explored and correlations are found for profile fit parameters with properties of the CCD frames. These were utilized to constrain the image profile fit models and adequately describe the observed point-spread function of stellar images with a minimum number of free parameters. Using an appropriate model function, blended images of double stars could be fit successfully. UCAC3 positions are derived from two-dimensional image profile fits with a five-parameter, symmetric Lorentz profile model. Internal precisions of about 5 mas per coordinate and single exposure are found, which are degraded by the atmosphere to about 10 mas. However, systematic errors exceeding 100 mas are present in the x, y data which have been corrected in the astrometric reductions following the x, y data reduction step described here.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2010
Accession Number
ADA519258

Entities

People

  • Norbert Zacharias

Organizations

  • United States Naval Observatory

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Amplitude
  • Charge Transfer
  • Data Processing
  • Data Reduction
  • Detection
  • Detectors
  • Errors
  • Observation
  • Observatories
  • Precision
  • Standards
  • Star Position
  • Telescopes
  • Two Dimensional
  • Urban Areas

Fields of Study

  • Physics

Readers

  • Astronomy/Astrophysics
  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.