Machine Learning and Parallelism in the Reconstruction of LHCb and its Upgrade

The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on reconstructing decays of c- and b-hadrons. For Run II of the LHC, a new trigger strategy with a real-time reconstruction, alignment and calibration was employed. This was made possible by implementing an offline-like track reconstruction in the high level trigger. However, the ever increasing need for a higher throughput and the move to parallelism in the CPU architectures in the last years necessitated the use of vectorization techniques to achieve the desired speed and a more extensive use of machine learning to veto bad events early on. This document discusses selected improvements in computationally expensive parts of the track reconstruction, like the Kalman filter, as well as an improved approach to get rid of fake tracks using fast machine learning techniques. In the last part, a short overview of the track reconstruction challenges for the upgrade of LHCb, is given. Running a fully software-based trigger, a large gain in speed in the reconstruction has to be achieved to cope with the 40 MHz bunch-crossing rate. Two possible approaches for techniques exploiting massive parallelization are discussed