Classification with Sparse Timeseries

Details Classification with Sparse Timeseries Classification with Sparse Timeseries

Sep 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011head...12.0603m&link_type=abstract

American Astronomical Society, HEAD meeting #12, #6.03

Astronomy and Astrophysics

Astronomy

Scientific paper

Timedomain astronomy has rapidly emerged as an interesting and important subfield of astronomy. Transients found range from solar system objects, to supernovae, to blazars thus touching upon astronomy at all scales and wavelengths. While a dense timeseries provides detailed information about the underlying astronomical source, when one is after classifying transients in realtime one does not have access to many data points. One then has to use archival information at all possible wavelengths and priors of various different types of objects to determine the closest matching class. This is an ideal Bayesian problem. We have been experimenting with such methods that make optimal use of whatever little amount of data that is available. We provide details of these methods and present results from the Catalina Realtime Transient Survey (CRTS) which publishes its transients in realtime.
The research is partially funded by grants from NASA and NSF.

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