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Profile picture of Leif Lönnblad. Photo.

Leif Lönnblad

Professor of Theoretical Physics

Profile picture of Leif Lönnblad. Photo.

Self-organizing networks for extracting jet features

Author

  • Leif Lönnblad
  • Carsten Peterson
  • Hong Pi
  • Thorsteinn Rögnvaldsson

Summary, in English

Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b, c and light quarks.

Department/s

  • Department of Astronomy and Theoretical Physics

Publishing year

1991-12

Language

English

Pages

193-209

Publication/Series

Computer Physics Communications

Volume

67

Issue

2

Document type

Journal article

Publisher

Elsevier

Topic

  • Subatomic Physics

Status

Published

ISBN/ISSN/Other

  • ISSN: 0010-4655