• DocumentCode
    1932067
  • Title

    Kink recognition with neural networks

  • Author

    Stimpfl-Abele, G.

  • Author_Institution
    Univ. Blaise Pascal, Aubiere, France
  • fYear
    1992
  • fDate
    25-31 Oct 1992
  • Firstpage
    838
  • Abstract
    The task of finding decays of charged tracks inside a tracking device is divided into two parts. First, a neural network is used to recognize kinks in well-constructed tracks. The inputs to this classification network are the residuals and the curvature obtained by a one-track fit. If a kink has been found, the same inputs are fed into a second neural network, which gives the radial position of the decay vertex. Both algorithms use feedforward nets with error backpropagation. Very good performance is found in comparison with conventional methods
  • Keywords
    feedforward neural nets; pattern recognition; physics computing; position sensitive particle detectors; proportional counters; charged tracks; classification network; decay vertex; error backpropagation; feedforward nets; kink recognition; neural networks; one-track fit; radial position; track decays; tracking device; Discrete event simulation; Feedforward systems; Mesons; Monte Carlo methods; Neural networks; Pattern recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nuclear Science Symposium and Medical Imaging Conference, 1992., Conference Record of the 1992 IEEE
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-0884-0
  • Type

    conf

  • DOI
    10.1109/NSSMIC.1992.301445
  • Filename
    301445