Combinatorial Kalman Filter and High Level Trigger Reconstruction for the Belle II Experiment

Combinatorial Kalman filters are a standard tool today for pattern recognition and charged particle reconstruction in high energy physics. In this thesis the implementation of the track finding software for the Belle II experiment and first studies on early Belle II data are presented. The track finding algorithm exploits novel concepts such as multivariate track quality estimates to form charged trajectory hypotheses combining information from the Belle II central drift chamber with the inner vertex sub-detectors. The eventual track candidates show an improvement in resolution on the parameters describing their spatial and momentum properties by up to a factor of seven over the former legacy implementation. The second part of the thesis documents a novel way to determine the collision event null time T0 and the implementation of optimisation steps in the online reconstruction code, which proved crucial in overcoming the high level trigger limitations.

Nominated as an outstanding Ph.D. thesis by the Karlsruhe Institute of Technology, Karlsruhe, Germany Concise and accessible description of the implementation of a Combinatorial Kalman Filter for data-taking in HEP experiment Describes and studies a novel data transportation scheme for the high level trigger reconstruction Provides a guide for the systematic optimisation of online reconstruction code

Inhalt
Introduction.- Experimental Setup.- Foundations.- Fast Reconstruction for the High Level Trigger.- Event Timing.- Combinatorial Kalman Filter.- Conclusion.- Bibliography.