for the sample of sources that belong to the cluster, and the full set of variables pertaining to the proper motion and CM subspaces. However, although normal densities are good approximations to the distribution of data in the space of proper motions, these do not model the distribution in the CMDs satisfactorily . From stellar evolution theory we know that this distribution follows a narrow set of isochrones that can be represented as a multivariate curve in the colour-magnitude subspace. The mixture-of-Gaussians likelihood model requires three components (as assessed by the BIC) to reproduce the curvilinear distribution, and the resulting covariances are too large to accommodate the curvilinear nature of the distribution. This results in conspicuously contaminated membership lists, and therefore we do not describe this set of models further.

We obtained a curvilinear likelihood model by fitting a principal curve [@hastie:principal] to the list of members. Principal curves analysis is one of the various extensions of principal components analysis to non-linear probability distributions that aims at minimising the sum of square deviations in all variables in representation space. This is accomplished with an iterative scheme whereby the current version of the curve (initially the first principal component) is modified 