Towards an Automated Classification of Transient Events in Synoptic Sky Surveys

Understand

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys.

• The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data.
• The classifications are improved iteratively as the new measurements are obtained.
• One novel feature is the development of an automated follow-up recommendation engine, that suggest those measurements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost functions.