Probabilistic Cross-Identification of Cosmic Events

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  • 27 February 2011
Abstract
We discuss a novel approach to identifying cosmic events in separate and independent observations. In the focus are the true astronomical transients, such as supernova explosions, that happen once, hence, whose measurements are not repeatable. Their classification and analysis have to make the best use of the available data. Bayesian hypothesis testing is used to associate events in space and time. Probabilities to the matching events are assigned by studying their rates of the occurrence. A case study of Type Ia supernovae shows that constraints from realistic lightcurves are accurately approximated by analytic formulas, which makes the process very efficient. Model-dependent associations are computationally more demanding but can boost the confidences.

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