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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)

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.

Keywords

Topographic mapping eclipsing binary stars 

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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

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