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Published March 2008 | public
Journal Article

Automated probabilistic classification of transients and variables


There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic) follow-up facilities with varied capabilities in terms of instruments, depth, cadence, wavelengths, etc., most of which are geared toward some specific astrophysical phenomenon. As the number of detected transient events grows, an automated, probabilistic classification of the detected variables and transients becomes increasingly important, so that an optimal use can be made of follow-up facilities, without unnecessary duplication of effort. We describe a methodology now under development for a prototype event classification system; it involves Bayesian and Machine Learning classifiers, automated incorporation of feedback from follow-up observations, and discriminated or directed follow-up requests. This type of methodology may be essential for the massive synoptic sky surveys in the future.

Additional Information

© 2008 Wiley. Received 2007 Sep 1; accepted 2007 Nov 27; Published online 2008 Feb 25. We are grateful to the members of the PQ survey team, and to the staff of Palomar Observatory. This work was supported in part by the NSF grants AST-0407448, AST-0326524, and CNS-0540369, and by the Ajax Foundation. SGD acknowledges a stimulating atmosphere of the Aspen Center for Physics. Finally, we thank the workshop organizers for an excellent and productive meeting.

Additional details

August 22, 2023
October 20, 2023