TY - JOUR
T1 - Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream
AU - Narayan, Gautham
AU - Zaidi, Tayeb
AU - Soraisam, Monika D.
AU - Wang, Zhe
AU - Lochner, Michelle
AU - Matheson, Thomas
AU - Saha, Abhijit
AU - Yang, Shuo
AU - Zhao, Zhenge
AU - Kececioglu, John
AU - Scheidegger, Carlos
AU - Snodgrass, Richard T.
AU - Axelrod, Tim
AU - Jenness, Tim
AU - Maier, Robert S.
AU - Ridgway, Stephen T.
AU - Seaman, Robert L.
AU - Evans, Eric Michael
AU - Singh, Navdeep
AU - Taylor, Clark
AU - Toeniskoetter, Jackson
AU - Welch, Eric
AU - Zhu, Songzhe
N1 - Publisher Copyright:
© 2018. The American Astronomical Society. All rights reserved..
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - data analysis-methods
KW - general-supernovae
KW - general-surveys-virtual observatory tools
KW - methods
KW - statistical-stars
KW - variables
UR - http://www.scopus.com/inward/record.url?scp=85047260546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047260546&partnerID=8YFLogxK
U2 - 10.3847/1538-4365/aab781
DO - 10.3847/1538-4365/aab781
M3 - Article
AN - SCOPUS:85047260546
SN - 0067-0049
VL - 236
JO - Astrophysical Journal, Supplement Series
JF - Astrophysical Journal, Supplement Series
IS - 1
M1 - 9
ER -