Original Paper

Object classification at the Nearby Supernova Factory

  1. S. Bailey,
  2. C. Aragon1,
  3. R. Romano1,2,
  4. R. C. Thomas1,
  5. B. A. Weaver1,3,
  6. D. Wong1

Article first published online: 4 MAR 2008

DOI: 10.1002/asna.200710932

Issue

Astronomische Nachrichten

Astronomische Nachrichten

Volume 329, Issue 3, pages 292–294, March 2008

Additional Information

How to Cite

Bailey, S., Aragon, C., Romano, R., Thomas, R. C., Weaver, B. A. and Wong, D. (2008), Object classification at the Nearby Supernova Factory. Astron. Nachr., 329: 292–294. doi: 10.1002/asna.200710932

Author Information

  1. 1

    Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA

  2. 2

    Luis W. Alvarez Fellow, National Energy Research Scientific Computing Center, 1 Cyclotron Road, Berkeley, CA 94720, USA

  3. 3

    University of California, Space Sciences Laboratory, Berkeley, CA 94720, USA

Publication History

  1. Issue published online: 4 MAR 2008
  2. Article first published online: 4 MAR 2008
  3. Manuscript Accepted: 27 NOV 2007
  4. Manuscript Received: 16 AUG 2007

Funded by

  • Office of High Energy and Nuclear Physics
  • U.S. Department of Energy. Grant Number: DE-FG02-92ER40704
  • Gordon & Betty Moore Foundation

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

Get PDF (100K)

Keywords:

  • methods: data analysis;
  • methods: statistical;
  • techniques: image processing

Abstract

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. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Get PDF (100K)