A Probabilistic Approach to Finding Geometric Objects in Spatial Datasets of the Milky Way

  • Jon Purnell
  • Malik Magdon-Ismail
  • Heidi Jo Newberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)

Abstract

Data from the Sloan Digital Sky Survey has given evidence of structures within the Milky Way halo from other nearby galaxies. Both the halo and these structures are approximated by densities based on geometric objects. A model of the data is formed by a mixture of geometric densities. By using an EM-style algorithm, we optimize the parameters of our model in order to separate out these structures from the data and thus obtain an accurate dataset of the Milky Way.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yanny, B., Newberg, H.J.: The ghost of sagittarius and lumps in the halo of the milky way. The Astrophysical Journal (2002)Google Scholar
  2. 2.
    Yanny, B., Newberg, H.J.: Sagittarius tidal debris 90 kiloparsecs from the galactic center. The Astrophysical Journal (2003)Google Scholar
  3. 3.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
  4. 4.
    Brent, R.P.: Algorithms for Minimization without Derivatives. Prentice-Hall, Englewood Cliffs (1973)MATHGoogle Scholar
  5. 5.
    Jorgensen, M., Hunt, L.: Mixture model clustering for mixed data with missing information. Computational Statistics and Data Analysis (2003)Google Scholar
  6. 6.
    Reina, C., Bradley, P., Fayyad, U.: Clustering very large databases using em mixture models. In: Proc. 15th International Conference on Pattern Recognition (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jon Purnell
    • 1
  • Malik Magdon-Ismail
    • 1
  • Heidi Jo Newberg
    • 1
  1. 1.Rensselaer Polytechnic Institute 

Personalised recommendations