LHCb [1] is a general purpose forward detector at CERN’s Large Hadron Collider. To further extend the scientific reach of the experiment, a future upgrade is foreseen to be installed in 2030, which is intended to be able to cope with a factor of 10 increase in the number of proton-proton collisions per second. Studies of the technology for this future detector are now underway.
Nikhef [2] is the Dutch institute for sub-atomic physics and its LHCb research group is involved in designing the Vertex Detector (VP2) for LHCb’s 2030 upgrade.
For the physics research to be possible, it is of critical importance that trajectories of particles traversing the future VP2 detector can be found with high probability in the raw pixel data.
This project proposes to extend an existing algorithm that reconstructs particle trajectories [3] by augmenting it with information about the measured time of traversal of particles.
The initial goal of the project is a working algorithm that can be used to optimize the design of the VP2 detector. A secondary goal is to optimize the performance of the developed algorithm to approach the goal of processing data from at least 100,000 batches of 50 simultaneous collisions per second on a fully-loaded PC-server in 2030.
Assessment of the performance of a pattern-recognition algorithm that has been augmented with the time-of-traversal of particles in terms of computational performance and ability to find trajectories.