The term used for a system that sits between a source and a customer
while adding value is a broker and that is how we characterize
ANTARES. It has to live in a larger time-domain ecosystem in order to
be of most value. The surveys themselves are the alert generators.
They take the images, perform image differencing, identify sources
that have changed, evaluate whether sources are artifacts or real, and
disseminate the alerts.
ANTARES takes these alerts and annotates them
with catalog information from objects associated with the alerts
across the full electromagnetic spectrum as well as past history of
the sources producing the alerts. It uses features, measured or
derived quantities or qualities of the alerts, to characterize them
into coarse bins. Objects that are relatively mundane or do not
require rapid follow up are diverted. These are not lost, but rather
stored in a database that could be used as a resource to study
variable sources that do not require immediate attention. ANTARES
then ranks the remaining sources by evaluating a measure of how rare
they are and distributes these alerts.
Downstream from ANTARES are other brokers that can take advantage of
the ANTARES annotations for more refined characterization or
classification. These could even be copies of the ANTARES
infrastructure, but with different filter sets. ANTARES ranked alerts
could trigger robotic or manual follow-up observations. Many of these
activities that handle alerts subsequent to ANTARES will require other
types of software infrastructure to implement them efficiently.
Software systems similar to ANTARES already exist, but they are
typically tuned to specific programs and still require a large human
involvement. ANTARES aims to be scalable to the LSST rate and volume
will also being as general as possible to fulfill the broader goals of
the US astronomical community. It will be a public resource.