FILTER METHODS IN TRACK AND VERTEX RECONSTRUCTION

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Abstract
After a review of widely used pattern recognition methods we present the Kalman filter and the associated smoother as a recursive variant of conventional least squares estimators. We first discuss its application to the reconstruction of charged tracks, including simultaneous track finding and track fitting and a robustification of the filter. This section is concluded by a case study of track reconstruction strategy in the DELPHI experiment. The second part deals with vertex reconstruction, including the detection of outlying tracks. It is shown that the detection of secondary vertices can be further improved by a robustification of the vertex fit via the M-estimator.

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