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Astrometric Reduction of the Wide-Field Images

  • Volodymyr Akhmetov
  • Sergii KhlamovEmail author
  • Vladislav Khramtsov
  • Artem Dmytrenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

In this paper we presented the new algorithm for astrometric reduction of the images received from modern large telescopes with very wide field of view. This algorithm is based on the iterative using of the method of ordinary least squares (OLS) and statistical Student t-criterion. The paper contains information about selecting the appropriate reduction model and system of conditional equations for determination of the reduction model parameters with the aim to solve it using the OLS method. The proposed algorithm also provides the automatic selection of the most probabilistic reduction model. At the first iteration the fifth degree polynomial is used, which provides 21 constant plates. The reduction error from the each iteration is used in the next iteration to eliminate reference objects whose residuals more than three sigma. In the developed algorithm the statistical Student t-criterion of reliability is applied after several iterations of getting rid of the noisy objects. This approach allows us to eliminate almost all systematic errors that are caused by imperfections of the optical system of modern large telescopes. The developed software based on the proposed algorithm was applied to perform the astrometric reduction of all measured positions of objects on the digitized photographic plates of SuperCOSMOS data. The research results showed that the new proposed algorithm allows performing the reduction into the system of reference catalogue with the highest accuracy level.

Keywords

Database Big data Catalogue Astrometry Reduction Wide field Data analysis 

Notes

Acknowledgment

This research has made using the data obtained from the SuperCOSMOS Science Archive, prepared and hosted by the Wide Field Astronomy Unit, Institute for Astronomy, University of Edinburgh, which is funded by the UK Science and Technology Facilities Council.

This work has made using the data from the European Space Agency (ESA) mission Gaia [30], processed by the Gaia Data Processing and Analysis Consortium (DPAC) [31]. Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement [32].

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.V. N. Karazin Kharkiv National UniversityKharkivUkraine

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