Abstract
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.
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References
Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10(1), 215–234 (1998)
Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
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)
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)
Hilditch, R.W.: An introduction to close binary stars. Cambridge University Press, Cambridge (2001)
Karttunen, H., Krger, P., Oja, H., Poutanen, M., Donner, K.J. (eds.): Fundamental astronomy. Springer, Heidelberg (1996)
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)
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)
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)
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)
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)
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)
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Gianniotis, N., Tiňo, P., Spreckley, S., Raychaudhury, S. (2009). Topographic Mapping of Astronomical Light Curves via a Physically Inspired Probabilistic Model. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_59
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DOI: https://doi.org/10.1007/978-3-642-04274-4_59
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