3 research outputs found
ANTARES: Progress towards building a `Broker' of time-domain alerts
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is
a joint effort of NOAO and the Department of Computer Science at the University
of Arizona to build prototype software to process alerts from time-domain
surveys, especially LSST, to identify those alerts that must be followed up
immediately. Value is added by annotating incoming alerts with existing
information from previous surveys and compilations across the electromagnetic
spectrum and from the history of past alerts. Comparison against a knowledge
repository of properties and features of known or predicted kinds of variable
phenomena is used for categorization. The architecture and algorithms being
employed are described
Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
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.Comment: 33 pages, 14 figures, submitted to ApJ