Investigation of structures near the J/ψJ/ψ mass threshold in 13 TeV proton proton collisions in the CMS experiment and phase two upgrade studies of the highgranularity calorimeter

This study, presents work on the study of structures near the J/ψJ/ψ mass threshold in 13 TeV proton-proton collisions in the CMS experiment and phase two upscaling studies of the High Granularity Calorimeter. The High Luminosity stage of the Large Hadron Collider will provide 10 times more total luminosity than the current collider. This will interfere with efficient data acquisition, such as radiation tolerance and event accumulation in detectors for advanced calorimetry. To remedy this unfavourable situation, a High Grain Calorimeter is being designed to replace the existing end-cap calorimeters. It will have high readout and triggering capability for both electromagnetic and hadronic sections. The electromagnetic section will consist of unique high precision 0.5-1cm^2 cell size hexagonal silicon sensors. Beam testing for the first hexagonal silicon modules was performed at CERN in 2016. The results of these tests in terms of system stability, calibration with ionising particles and resolution for electrons, and the comparison of these quantities with GEANT4-based simulations are presented, as well as the detector control system designed for the beam test system. It presents the analysis performed to search for narrow resonances decaying to J/ψ J/ψ µ^+ µ^- µ^+ µ^- characterised by a four-Müon final state. The analysis uses data sets from proton-proton collisions at √s = 13 TeV collected with the CMS detector at the LHC in 2017 and 2018, corresponding to a total luminosity of 111.7 fb^(-1). The J/ψ reconstruction is realised via its decay into a Müon pair of opposite sign. The other Müon pair is considered as potentially offshell J/ψ and their invariant mass should be below the J/ψ mass value. In this analysis, a new strategy based on event mixing is introduced to minimise the pileup and physics backgrounds; this strategy is fully data-driven and intrinsically blind.