Galaxy Decomposition in Multispectral Images Using Markov Chain Monte Carlo Algorithms

  • Benjamin Perret
  • Vincent Mazet
  • Christophe Collet
  • Éric Slezak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


Astronomers still lack a multiwavelength analysis scheme for galaxy classification. In this paper we propose a way of analysing multispectral observations aiming at refining existing classifications with spectral information. We propose a global approach which consists of decomposing the galaxy into a parametric model using physically meaningful structures. Physical interpretation of the results will be straightforward even if the method is limited to regular galaxies. The proposed approach is fully automatic and performed using Markov Chain Monte Carlo (MCMC) algorithms. Evaluation on simulated and real 5-band images shows that this new method is robust and accurate.


Bayesian inference MCMC multispectral image processing galaxy classification 


  1. 1.
    De Vaucouleurs, G.: Classification and Morphology of External Galaxies. Handbuch der Physik 53, 275 (1959)CrossRefGoogle Scholar
  2. 2.
    Yagi, M., Nakamura, Y., Doi, M., Shimasaku, K., Okamura, S.: Morphological classification of nearby galaxies based on asymmetry and luminosity concentration. Monthly Notices of Roy. Astr. Soc. 368, 211–220 (2006)CrossRefGoogle Scholar
  3. 3.
    Petrosian, V.: Surface brightness and evolution of galaxies. Astrophys. J. Letters 209, L1–L5 (1976)CrossRefGoogle Scholar
  4. 4.
    Abraham, R.G., Valdes, F., Yee, H.K.C., van den Bergh, S.: The morphologies of distant galaxies. 1: an automated classification system. Astrophys. J. 432, 75–90 (1994)CrossRefGoogle Scholar
  5. 5.
    Conselice, C.J.: The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories. Astrophys. J. Suppl. S. 147, 1–28 (2003)CrossRefGoogle Scholar
  6. 6.
    Kelly, B.C., McKa, T.A.: Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey. Astron. J. 127, 625–645 (2004)CrossRefGoogle Scholar
  7. 7.
    Baillard, A., Bertin, E., Mellier, Y., McCracken, H.J., Géraud, T., Pelló, R., Leborgne, F., Fouqué, P.: Project EFIGI: Automatic Classification of Galaxies. In: Astron. Soc. Pac. Conf. ADASS XV, vol. 351, p. 236 (2006)Google Scholar
  8. 8.
    Allen, P.D., Driver, S.P., Graham, A.W., Cameron, E., Liske, J., de Propris, R.: The Millennium Galaxy Catalogue: bulge-disc decomposition of 10095 nearby galaxies. Monthly Notices of Roy. Astr. Soc. 371, 2–18 (2006)CrossRefGoogle Scholar
  9. 9.
    Tsalmantza, P., Kontizas, M., Bailer-Jones, C.A.L., Rocca-Volmerange, B., Korakitis, R., Kontizas, E., Livanou, E., Dapergolas, A., Bellas-Velidis, I., Vallenari, A., Fioc, M.: Towards a library of synthetic galaxy spectra and preliminary results of classification and parametrization of unresolved galaxies for Gaia: Astron. Astrophys. 470, 761–770 (2007)Google Scholar
  10. 10.
    Bazell, D.: Feature relevance in morphological galaxy classification. Monthly Notices of Roy. Astr. Soc. 316, 519–528 (2000)CrossRefGoogle Scholar
  11. 11.
    Kelly, B.C., McKay, T.A.: Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey. II. Multiwavelength Classification. Astron. J. 129, 1287–1310 (2005)Google Scholar
  12. 12.
    Lauger, S., Burgarella, D., Buat, V.: Spectro-morphology of galaxies: A multi-wavelength (UV-R) classification method. Astron. Astrophys. 434, 77–87 (2005)CrossRefGoogle Scholar
  13. 13.
    Simard, L., Willmer, C.N.A., Vogt, N.P., Sarajedini, V.L., Phillips, A.C., Weiner, B.J., Koo, D.C., Im, M., Illingworth, G.D., Faber, S.M.: The DEEP Groth Strip Survey. II. Hubble Space Telescope Structural Parameters of Galaxies in the Groth Strip. Astrophys. J. Suppl. S. 142, 1–33 (2002)CrossRefGoogle Scholar
  14. 14.
    de Souza, R.E., Gadotti, D.A., dos Anjos, S.: BUDDA: A New Two-dimensional Bulge/Disk Decomposition Code for Detailed Structural Analysis of Galaxies. Astrophys. J. Suppl. S. 153, 411–427 (2004)CrossRefGoogle Scholar
  15. 15.
    Peng, C.Y., Ho, L.C., Impey, C.D., Rix, H.-W.: Detailed Structural Decomposition of Galaxy Images. Astron. J. 124, 266–293 (2002)CrossRefGoogle Scholar
  16. 16.
    Sérsic, J.L.: Atlas de galaxias australes. Cordoba, Argentina: Observatorio Astronomico (1968)Google Scholar
  17. 17.
    Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo In Practice. Chapman & Hall/CRC, Washington (1996)zbMATHGoogle Scholar
  18. 18.
    Gilks, W.R., Roberts, G.O., Sahu, S.K.: Adaptive Markov chain Monte Carlo through regeneration. J. Amer. Statistical Assoc. 93, 1045–1054 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Roberts, G.O., Gilks, W.R.: Convergence of adaptive direction sampling. J. of Multivariate Ana. 49, 287–298 (1994)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Mazet, V., Brie, D., Idier, J.: Simulation of positive normal variables using several proposal distributions. In: IEEE Workshop on Statistical Sig. Proc., pp. 37–42 (2005)Google Scholar
  21. 21.
    Devroye, L.: Non-Uniforme Random Variate Generation. Springer, New York (1986)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Benjamin Perret
    • 1
  • Vincent Mazet
    • 1
  • Christophe Collet
    • 1
  • Éric Slezak
    • 2
  1. 1.LSIIT (UMR CNRS-Université de Strasbourg 7005)France
  2. 2.Laboratoire Cassiopée (UMR CNRS-Observatoire de la Côte d’Azur 6202)France

Personalised recommendations