Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream

Understand

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm.

• Alert-brokers -- automated software system to sift through, characterize, annotate and prioritize events for followup -- will be critical tools for managing alert streams in the LSST era.
• The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker.
• In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry.