Track reconstruction using graph neural networks

After more than a decade of data-taking of the experiments at the Large Hadron Collider at CERN, no evidence of New Physics has been found. A new accelerator, the High-Luminosity Large Hadron Collider is currently under construction, increasing the instantaneous luminosity by one order of magnitud. The new accelerator conditions generate a challenging environment for the detectors at the LHC. One of the most important difficulties that the CMS experiment will find is the extremely large hit occupancy in the tracker and the huge combinatorial problem associated to the track reconstruction. This is even more difficult for displaced topologies emerging from potential new, long-lived particles, where the traditional seeding methods cannot be used. This work explores the use of Graph Neural Networks as an alternative procedure to identify tracks in a detector. A simple track generator has been developed together with a simplified model of the CMS tracker, and the resulting tracks and hits are reconstructed using a dedicated GNN algorithm trained with a number of example tracks. The performance of the algorithm is tested to be higher than 80% even for 500 simultaneous tracks.