3 years ago

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

Thomas Matheson, Zhenge Zhao, Songzhe Zhu, Eric Welch, Tim Jenness, Eric Michael Evans, Robert L. Seaman, Tim Axelrod, Shuo Yang, Monika D. Soraisam, Navdeep Singh, Gautham Narayan, Stephen T. Ridgway, Tayeb Zaidi, Robert S. Maier, Abhijit Saha, Michelle Lochner, Richard T. Snodgrass, Clark Taylor, Carlos Scheidegger, John Kececioglu, Zhe Wang, Jackson Toeniskoetter

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. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

Publisher URL: http://arxiv.org/abs/1801.07323

DOI: arXiv:1801.07323v1

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