Neural tracking in ALICE

A neural network based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles ( p t >1 GeV /c ) is presented,. The model is based on the Denby-Peterson scheme, with some original improvements which are necessary to cope with the very high track density expected in ALICE. Results are shown for a central Pb–Pb event at 5.5 A TeV in the center of mass system and the comparison with the Kalman Filter results is included.

84.35.+i
ALICE experiment
Pattern recognition
Track reconstruction
Neural networks
talk: Moscow 2002/06/24
nucleus nucleus: colliding beams
drift chamber: tracks
neural network
track data analysis

References(15)

Figures(0)

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