Computer Science – Learning
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aspc..434..123w&link_type=abstract
Astronomical Data Analysis Software and Systems XIX. Proceedings of a conference held October 4-8, 2009 in Sapporo, Japan. Edite
Computer Science
Learning
Scientific paper
The US VAO Facility for Rapid Transients (if funded) will collect and disseminate observations about time-critical astronomical transients, and add annotations and intelligent machine-learning to those observations. The information can be “pushed” to subscribers, who may be either humans or machines that control telescopes. Subscribers can prepare precise “triggers” to decide which events should reach them and their machines, that may be based on the generic event, or on the specific vocabulary of parameters that define a particular type of observation. The system will not be centralized, but rather a set of interoperating nodes with caching. The twin thrusts are automation of process, and discrimination of interesting events.
Djorgovski Stanislav G.
Donalek Ciro
Drake Andrew J.
Graham Mary Jane
Mahabal Ashish A.
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