Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model

  • Nikolaos Gianniotis
  • Peter Tiňo
  • Steve Spreckley
  • Somak Raychaudhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)


We present a probabilistic generative approach for constructing topographic maps of light curves from eclipsing binary stars. The model defines a low-dimensional manifold of local noise models induced by a smooth non-linear mapping from a low-dimensional latent space into the space of probabilistic models of the observed light curves. The local noise models are physical models that describe how such light curves are generated. Due to the principled probabilistic nature of the model, a cost function arises naturally and the model parameters are fitted via MAP estimation using the Expectation-Maximisation algorithm. Once the model has been trained, each light curve may be projected to the latent space as the the mean posterior probability over the local noise models. We demonstrate our approach on a dataset of artificially generated light curves and on a dataset comprised of light curves from real observations.


Topographic mapping eclipsing binary stars 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10(1), 215–234 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  3. 3.
    Tiňo, P., Kaban, A., Sun, Y.: A generative probabilistic approach to visualizing sets of symbolic sequences. In: KDD 2004: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 701–706. ACM Press, New York (2004)Google Scholar
  4. 4.
    Gianniotis, N., Tiňo, P.: Visualisation of tree-structured data through generative probabilistic modelling. In: Verleysen, M. (ed.) European Symposium on Artificial Neural Networks, D-Facto, pp. 97–102 (2007)Google Scholar
  5. 5.
    Hilditch, R.W.: An introduction to close binary stars. Cambridge University Press, Cambridge (2001)CrossRefGoogle Scholar
  6. 6.
    Karttunen, H., Krger, P., Oja, H., Poutanen, M., Donner, K.J. (eds.): Fundamental astronomy. Springer, Heidelberg (1996)Google Scholar
  7. 7.
    Devor, J.: Solutions for 10,000 eclipsing binaries in the bulge fields of ogle ii using debil. The Astrophysical Journal 628(1), 411–425 (2005)CrossRefGoogle Scholar
  8. 8.
    Halbwachs, J.L., Mayor, M., Udry, S., Arenou, F.: Multiplicity among solar-type stars. iii. statistical properties of the f7-k binaries with periods up to 10 years. Astronomy and Astrophysics 397, 159–175 (2003)CrossRefGoogle Scholar
  9. 9.
    Miller, G.E., Scalo, J.M.: The initial mass function and stellar birthrate in the solar neighborhood. Astrophysical Journal Supplement Series 41, 513–547 (1979)CrossRefGoogle Scholar
  10. 10.
    Paczyński, B., Szczygieł, D.M., Pilecki, B., Pojmański, G.: Eclipsing binaries in the All Sky Automated Survey catalogue. Monthly Notices of the Royal Astronomical Society 368, 1311–1318 (2006)CrossRefGoogle Scholar
  11. 11.
    Ng, S., Krishnan, T., McLachlan, G.: The em algorithm. In: Gentle, J., Hardle, W., Mori, Y. (eds.) Handbook of Computational Statistics, vol. 1, pp. 137–168. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Rowe, J.E., Hidović, D.: An evolution strategy using a continuous version of the gray-code neighbourhood distribution. In: Deb, K., et al. (eds.) GECCO 2004, Part I. LNCS, vol. 3102, pp. 725–736. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nikolaos Gianniotis
    • 1
  • Peter Tiňo
    • 2
  • Steve Spreckley
    • 3
  • Somak Raychaudhury
    • 3
  1. 1.Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergHeidelbergGermany
  2. 2.School of Computer ScienceThe University of BirminghamEdgbastonUnited Kingdom
  3. 3.School of Physics and AstronomyThe University of BirminghamEdgbastonUnited Kingdom

Personalised recommendations