Object Classification at the Nearby Supernova Factory

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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 nonsupernova 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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Aragon, Cecilia R.; Bailey, Stephen; Aragon, Cecilia R.; Romano, Raquel; Thomas, Rollin C.; Weaver, B. A. et al. December 21, 2007.

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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 nonsupernova 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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  • Journal Name: Astronomische Nachrichten; Journal Volume: 329

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  • Report No.: LBNL-696E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 934989
  • Archival Resource Key: ark:/67531/metadc899734

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  • December 21, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 8, 2016, 1:15 p.m.

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Aragon, Cecilia R.; Bailey, Stephen; Aragon, Cecilia R.; Romano, Raquel; Thomas, Rollin C.; Weaver, B. A. et al. Object Classification at the Nearby Supernova Factory, article, December 21, 2007; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc899734/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.