  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.
