How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

Details How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aas...209.7809b&link_type=abstract

2007 AAS/AAPT Joint Meeting, American Astronomical Society Meeting 209, #78.09; Bulletin of the American Astronomical Society, V

Astronomy and Astrophysics

Astronomy

Scientific paper

We present the results of applying new object classification techniques to difference images in the context of the SNfactory supernova search. Most current supernova searches subtract reference images from new images, identify leftover objects, and apply simple threshold cuts on parameters such as statistical significance, shape, and motion to reject backgrounds such as cosmic rays, asteroids, and subtraction artifacts. This leaves a large number of non-supernova candidates which must be verified by human inspection before triggering additional followup.
In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better signal/background discrimination. At the SNfactory, we reduced the number of background candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for handling the large data volumes produced by upcoming projects such as PanSTARRS and LSST.

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