Abstract
The new track reconstruction program CATS developed for the Vertex Detector System of the HERA-B experiment at DESY is presented. It employs a cellular automaton for track searching and the Kalman filter for track fitting. This results in a very fast algorithm that combines highly efficient track recognition with accurate and reliable track parameter estimation. To reduce the computational cost of the fit an optimized numerical implementation of the Kalman filter is used. Alternative approaches to the track reconstruction in the VDS are also discussed. Since 1999, after extensive tests on simulated data, CATS has been employed to reconstruct experimental data collected in HERA-B. Results regarding tracking performance, the accuracy of track parameter estimates and CPU time consumption are presented.
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