Speaker
Description
The upgrade of the CMS apparatus for the HL-LHC will provide unprecedented timing measurement capabilities, in particular for charged particles through the Mip Timing Detector (MTD). One of the main goals of this upgrade is to compensate the deterioration of primary vertex reconstruction induced by the increased pileup of proton-proton collisions by separating clusters of tracks not only in space but also in time.
This contribution discusses the latest algorithmic developments to optimally exploit such new information. Modern machine-learning-based techniques are explored as a possible alternative to traditional approaches: graph neural network architectures are studied to simultaneously cluster particles and assign them the correct mass hypotheses, which is needed to correctly determine the time at the vertex.