model the distribution in the various two-dimensional projections without conspicuous biases. Several tests showed that principal curves obtained in a range of degrees of freedom are barely distinguishable from one another and produce the same candidate lists. When the iterations converged, the same smoothing scheme was used to define the variance $\Sigma(\lambda)$ along the curve.

The final likelihood-model in the CM subspace is defined as

$$p(\vec{m}_{CM}|{\cal C}_2)=p(\lambda)\cdot p({\vec{m}_{CM}}|\lambda),$$

where $p(\lambda)$ is interpolated from the empirical kernel density estimate of sources per ${\rm d}\lambda$, and $p({\vec{m}_{CM}}|\lambda)$ is modelled with a multivariate normal distribution, the covariance of which is again interpolated from the empirical distribution of covariances estimated from sets of sources within intervals of $\Delta\lambda$. This effectively overestimates the true covariance since the estimated values respond not only to the natural spread of the isochrone, but also to the measurement errors.

We found clear evidence of an isochrone parallel to (and brighter than) the main cluster component. This is interpreted as the result of the presence of equal mass binaries in the cluster sample. In general, the spread of the sequence is due to the combined effects of multiplicity (at all possible luminosity ratios), intrinsic variability, and rotation. We only modelled 