Real Time Classification of Transient Events in Synoptic Sky Surveys

Details Real Time Classification of Transient Events in Synoptic Sky Surveys Real Time Classification of Transient Events in Synoptic Sky Surveys

Jan 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21547703d&link_type=abstract

American Astronomical Society, AAS Meeting #215, #477.03; Bulletin of the American Astronomical Society, Vol. 42, p.564

Astronomy and Astrophysics

Astronomy

Scientific paper

In modern synoptic sky surveys, reliable and robust real-time classification of transient events is essential in order to exploit the full scientific potential of these surveys, and conduct optimized follow-up observations and additional measurements. This generally has to be done using sparse and heterogeneous data for individual events, both from the survey pipelines and existing archives. Besides, in order to catch some of the more interesting objects, the classification may need to be done using a minimal number of observations available. Here we describe a fusion network that combines the results from the different kinds of classifiers we are building (eg, Bayesian Networks, GPR, Neural Networks) that can be triggered by a specific event. Two of such classification methods involve Bayesian Networks with colors and contextual information, such as distance from a galaxy and galactic latitude and Gaussian Process Regression with lightcurves. Both are fast enough to be used in a real time pipeline and can deal with missing data and work with a small number of epochs. The high order fusion module can help to improve the classification, narrowing down the number of competing classes and leading to optimal follow-ups and new discoveries.

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