  We discuss a novel approach to identifying cosmic events in separate and
independent observations. In our focus are the true events, such as supernova
explosions, that happen once, hence, whose measurements are not repeatable.
Their classification and analysis have to make the best use of all the
available data. Bayesian hypothesis testing is used to associate streams of
events in space and time. Probabilities are assigned to the matches by studying
their rates of occurrence. A case study of Type Ia supernovae illustrates how
to use lightcurves in the cross-identification process. Constraints from
realistic lightcurves happen to be well-approximated by Gaussians in time,
which makes the matching process very efficient. Model-dependent associations
are computationally more demanding but can further boost our confidence.
