Object Classification in Astronomical Images

  • Richard L. White
Conference paper

Abstract

Automated classification methods are needed for processing the huge quantities of data generated by modern astronomical instruments. The star-galaxy classification problem and some techniques that have been applied to it are briefly reviewed. Methods for constructing training sets and selecting parameters are described.

A new method of scaling parameter values using ranks has been developed. This approach is found to be of great utility for distinguishing stars and galaxies on digitized photographic plates. It should be widely applicable to other classification problems, especially when the data being classified are not completely homogeneous.

Keywords

Expense Photographic Emulsion 

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References

  1. [DWF94]
    S. Djorgovski, N. Weir, and U. Fayyad. Processing and analysis of the Palomar-STScI Digital Sky Survey using a novel software technology. In D. R. Crabtree, R. J. Hanisch, and J. Barnes. editors, Astronomical Data Analysis Software and Systems III. pages 195–204, San Francisco. 1994. ASP.Google Scholar
  2. [FWD93]
    U. M. Fayyad, N. Weir, and S. Djorgovski. SKICAT: A machine learning system for automated cataloging of large scale sky surveys. In Proceedings of the Tenth International Conference on Machine Learning, pages 112–119, Amherst, MA. 1993. Morgan Kaufmann.Google Scholar
  3. [GK92]
    J. E. Gunn and G. R. Knapp. The Sloan Digital Sky Survey. Proceed-ings of the Astronomical Society of the Pacific. 43:267–279, 1992.Google Scholar
  4. [HKS93]
    D. Heath, S. Kasif, and S. Salzberg. Learning oblique decision trees. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1002–1007, Chambery. France, 1993. Morgan Kaufmann.Google Scholar
  5. [LMJ+95]
    B. M. Lasker. B. J. McLean, H. Jenkner, M. G. Lattanzi, and A. Spagna. Potential application of GSC-II for GAIA operations. In F. van Leeuven and M. Perryman. editors. Future Possibilities for Astrornetry in Space. ESA SP-379, 1995.Google Scholar
  6. [LSM+90]
    B. M. Lasker, C. R. Sturch, B. J. McLean, J. L. Russell, H. Jenkner, and M. M. Shara. The Guide Star Catalog. I. Astronomical foundations and image processing. Astronomical Journal, 99:2019. 1990.CrossRefGoogle Scholar
  7. [MKS94]
    S. K. Murthy, S. Kasif. and S. Salzberg. Induction of oblique decision trees. Journal of Artificial Intelligence Research, 2:1–33, 1994.MATHGoogle Scholar
  8. [OSP+92]
    S. C. Odewahn, E. B. Stockwell, R. L. Pennington, R. M. Humphreys, and W. A. Zumach. Automated star/galaxy descrimination with neural networks. Astronomical Journal, 103:318–331. 1992.CrossRefGoogle Scholar
  9. [PLG+96]
    M. Postman, L. M. Lubin, J. E. Gunn. J. B. Oke. J. G. Hoessel, D. P. Schneider, and J. A. Christensen. The Palomar distant cluster survey: I. The cluster catalog. Astronomical Journal, 111:615, 1996.CrossRefGoogle Scholar
  10. [SCF+95]
    S. Salzberg, R. Chandar, H. Ford, S. K. Murthy. and R. White. Decision trees for automated identification of cosmic rays in Hubble Space Telescope images. Proceedings of the Astronomical Society of the Pacific, 107:279–288, 1995.CrossRefGoogle Scholar
  11. [Wei94]
    N. Weir. Automated analysis of the digitized Second Palomar sky sur-vey: system design, implementation, and initial results. PhD thesis. California Institute of Technology, Pasadena, California. 1994.Google Scholar

References

  1. [Va182]
    Francisco Valdes. Resolution classifier. In David L. Crawford, editor, SPIE Vol. 331 Instrumentation in Astronomy IV,pages 465–472. SPIE, SPIE, March 1982.Google Scholar
  2. [Va189]
    Francisco Valdes. Faint object classification and analysis system standard test images results. In P. J. Grosbol, F. Murtagh, and R. H. Warmels, editors, 1st ESO/ST-ECF Data Analysis Workshop (ESO Conference and Workshope Proceedings No. 31),pages 35–67. ESO, ESO, September 1989.Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Richard L. White

There are no affiliations available

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