An Evolutionary Approach to Modeling Radial Brightness Distributions in Elliptical Galaxies

  • Jin Li
  • Xin Yao
  • Colin Frayn
  • Habib G. Khosroshahi
  • Somak Raychaudhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

A reasonably good description of the luminosity profiles of galaxies is needed as it serves as a guide towards understanding the process of galaxy formation and evolution. To obtain a radial brightness profile model of a galaxy, the way varies both in terms of the exact mathematical form of the function used and in terms of the algorithm used for parameters fitting for the function given. Traditionally, one builds such a model by means of fitting parameters for a functional form assumed beforehand. As a result, such a model depends crucially on the assumed functional form. In this paper we propose an approach that enables one to build profile models from data directly without assuming a functional form in advance by using evolutionary computation. This evolutionary approach consists of two major steps that serve two goals. The first step applies the technique of genetic programming with the aim of finding a promising functional form, whereas the second step takes advantage of the power of evolutionary programming with the aim of fitting parameters for functional forms found at the first step. The proposed evolutionary approach has been applied to modeling 18 elliptical galaxies profiles and its preliminary results are reported in this paper.

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References

  1. 1.
    Andredakis, Y.C., Sanders, R.H.: Exponential bulges in late-type spirals: an improved description of the light distribution. Monthly Notices of the Royal Astronomical Society (MNRAS) 267(2), 283–296 (1994)Google Scholar
  2. 2.
    De Jong, R.S.: Near-IR photometry of 86 galaxies. II. Astronomy & Astrophysics Supplement Series 118, 557–573 (1996)CrossRefGoogle Scholar
  3. 3.
    De Vaucouleurs, G.: Recherches sur les Nebuleuses Extragalactiques. Annales d’Astrophysique 11, 247 (1948)Google Scholar
  4. 4.
    Fogel, D.B.: System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Needham Heights, MA: Ginn (1991)Google Scholar
  5. 5.
    Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)MATHGoogle Scholar
  6. 6.
    Freeman, K.C.: On the Disks of Spiral and S0 Galaxies. The Astrophysical Journal 160, 811–830 (1970)CrossRefGoogle Scholar
  7. 7.
    Hubble, E.P.: Distribution of luminosity in elliptical nebulae. The Astrophysical Journal 71, 231–276 (1930)CrossRefGoogle Scholar
  8. 8.
    Khosroshahi, H.G., Raychaudhury, S., Ponman, T.J., Miles, T.A., Forbes, D.A.: Scaling relations in early-type galaxies belonging to groups. Monthly Notices of the Royal Astronomical Society 349, 527–534 (2004)CrossRefGoogle Scholar
  9. 9.
    Khosroshahi, H.G., Wadadekar, Y., Kembhavi, A., Mobasher, B.: Correlations among global photometric properties of disk galaxies. The Astrophysical Journal letters 531, 103–106 (2000a)CrossRefGoogle Scholar
  10. 10.
    Khosroshahi, H.G., Wadadekar, Y., Kembhavi, A.: Correlations among global photometric properties of disk galaxies. The Astrophysical Journal 533, 162–171 (2000b)CrossRefGoogle Scholar
  11. 11.
    King, I.R.: The structure of star clusters. I. An empirical density law. Astronomical Journal 67, 471 (1962)CrossRefGoogle Scholar
  12. 12.
    King, I.R.: The structure of star clusters. III. Some simple dynamical models. Astronomical Journal 71, 64Google Scholar
  13. 13.
    Kormendy, J.: Brightness distributions in compact and normal galaxies. III - Decomposition of observed profiles into spheroid and disk components. The Astrophysical Journal 217, 406–419 (1977)CrossRefGoogle Scholar
  14. 14.
    Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  15. 15.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)MATHGoogle Scholar
  16. 16.
    Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  17. 17.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs (1994)Google Scholar
  18. 18.
    Schombert, J.M., Bothun, G.D.: The methodology and reliability of determining bulge-to-disk ratios for spiral galaxies. Astronomical Journal (ISSN 0004-6256) 93, 60–73 (1987)Google Scholar
  19. 19.
    Sersic, J.L.: Atlas de Galaxies Australes Cordoba: Observatorio Astronomica (1968)Google Scholar
  20. 20.
    Sparke, L.S., Gallagher III, J.S.: Galaxies in the Universe: An introduction. Cambridge University Press, Cambridge (2000)Google Scholar
  21. 21.
    Van den Bergh, S.: Galaxy Morphology and classification. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  22. 22.
    Yao, X.: An overview of evolutionary computation. Chinese J. Adv. Software Res. 3(1), 12–29 (1996)Google Scholar
  23. 23.
    Yao, X., Liu, Y., Lin, G.: Evolutionary Programming Made Faster. IEEE Transaction on Evolutionary Computation 3(2), 82–102 (1999)CrossRefGoogle Scholar
  24. 24.
    Wadadekar, Y., Robbason, B., Kembhavi, A.: Two-dimensional Galaxy Image Decomposition. Astronomical Journal 117(3), 1219–1228 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jin Li
    • 1
  • Xin Yao
    • 1
  • Colin Frayn
    • 1
  • Habib G. Khosroshahi
    • 2
  • Somak Raychaudhury
    • 2
  1. 1.The Centre of Excellence for Research, in Computational Intelligence and Applications (CERCIA), School of Computer ScienceThe University of BirminghamEdgbaston, BirminghamUK
  2. 2.Astrophysics and Space Research Group, School of Physics and AstronomyThe University of BirminghamEdgbaston, BirminghamUK

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