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

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

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers- A utomated software system to sift through, characterize, annotate, and prioritize events for follow-up-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 versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although 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 toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

Original languageEnglish (US)
Article number9
JournalAstrophysical Journal, Supplement Series
Volume236
Issue number1
DOIs
Publication statusPublished - May 1 2018

    Fingerprint

Keywords

  • data analysis-methods
  • general-supernovae
  • general-surveys-virtual observatory tools
  • methods
  • statistical-stars
  • variables

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Narayan, G., Zaidi, T., Soraisam, M. D., Wang, Z., Lochner, M., Matheson, T., ... Zhu, S. (2018). Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream. Astrophysical Journal, Supplement Series, 236(1), [9]. https://doi.org/10.3847/1538-4365/aab781