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Analysing particle jets with artificial neural networks

  • K. -H. Becks
  • J. Dahm
  • F. Seidel
Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 604)

Abstract

Elementary particle physics includes a number of feature recognition problems for which artificial neural networks can be used. We used a feed-forward neural network to seperate particle jets originating from b-quarks from other jets. Some aspects such as pruning and overfitting have been studied. Furthermore, the influence of modifications in architecture and input space have been examined. In addtition we discuss how self-organizing networks can be applied to high energy physics problems.

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References

  1. [1]
    J. Hertz, A. Krogh, R. G. Palmer Introduction to the Theory of Neural Computation, Addison-Wesley (1991)Google Scholar
  2. [2]
    D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986)Google Scholar
  3. [3]
    L. Lönnblad, C. Peterson, H. Pi, T. Rögnvaldsson Self-organizing Networks for Extracting Jet Features, Preprint Lund University, LU TP 91-4 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • K. -H. Becks
    • 1
  • J. Dahm
    • 1
  • F. Seidel
    • 1
  1. 1.Physics DepartmentUniversity of WuppertalWuppertalGermany

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