Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
Soraisam, Monika D.
Snodgrass, Richard T.
Maier, Robert S.
Ridgway, Stephen T.
Seaman, Robert L.
Evans, Eric Michael
AffiliationUniv Arizona, Dept Comp Sci
Univ Arizona, Steward Observ
Univ Arizona, Dept Math
Univ Arizona, Lunar & Planetary Lab
Keywordsmethods: data analysis
stars: variables: general
virtual observatory tools
MetadataShow full item record
PublisherIOP PUBLISHING LTD
CitationGautham Narayan et al 2018 ApJS 236 9
Rights© 2018. The American Astronomical Society. All rights reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe 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-automated 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.
VersionFinal published version
SponsorsLasker Fellowship at the Space Telescope Science Institute; SKA SA; NRF; AIMS; NSF INSPIRE grant [CISE AST-1344204]; NOAO Community Science Data Center (CSDC)