The LHCb experiment at CERN will undergo an internal transformation over the coming two years (2019-2021), during a maintenance and upgrade period known as Long Shutdown 2 (LS2). This improvement aims at extending the physics reach of the experiment allowing it to run with a proton–proton collision rate 5 times higher than before at which it will be working once the Large Hadron Collider (LHC) restarts in 2021.
The upgrade not only affects the sub-detectors but also the pattern recognition algorithms whose purpose is to reconstruct particle trajectories, since there is a need to cope with the increased complexity of the physical environment. This project aims at using Artificial Neural Networks and spatial indexing data structures, such as R-trees, to provide a different approach to track reconstruction with respect to the algorithms which are currently used in LHCb.