Adaptive methods with application to track reconstruction at LHC

The task of reconstructing particle tracks in a noisy environment leads to a combinatorial optimization problem that cannot be solved in an exhaustive way. The series will give an introduction into adaptive methods of solving such problems, which occur also in other fields like cluster analysis, image processing, or operations research. In the context of track reconstruction the adaptive solution can be formulated as an iteratively reweighted least-squares procedure (Kalman filter) with annealing. Examples of the performance of this algorithm to reconstruction problems in the ATLAS Inner Detector and the CMS Tracker will conclude the lecture series.

The most important concepts presented in the lecture series will be exemplified by basic but nevertheless representative problems. The solutions will show the benefit of the adaptive approach even in these simple cases.