Object classification at the Nearby Supernova Factory

Details Object classification at the Nearby Supernova Factory Object classification at the Nearby Supernova Factory

Mar 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008an....329..292b&link_type=abstract

Astronomische Nachrichten, Vol.329, Issue 3, p.292

Astronomy and Astrophysics

Astronomy

1

Methods: Data Analysis, Methods: Statistical, Techniques: Image Processing

Scientific paper

We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of non-supernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys.

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