UC San Diego

Two projects are presented in this thesis, the first pertaining to a physics analysis using the data from the CMS Experiment at the LHC, and the second pertaining to the development of a new parallelizable algorithm for reconstructing particle tracks in the outer tracker of the CMS Detector at the upcoming High Luminosity LHC.

A search for supersymmetric phenomena beyond the standard model in a final state containing an on-shell Z boson, jets and missing transverse energy is performed using a data sample of proton-proton collisions at $\sqrt{s} = 13$~TeV corresponding to an integrated luminosity of 137 $\ifb$ collected by the CMS Experiment at the LHC between 2016 and 2018. The observed event yields are consistent with standard model predictions in the signal regions. These results are then interpreted to constrain the masses of supersymmetric particles in the context of the signal models. Gluino masses up to 1870 GeV, and chargino (neutralino) masses up to 750 (800) GeV are excluded at the 95\% confidence level, which extends the reach over the previous results by a few hundred GeV.

The High Luminosity LHC (HL-LHC) will increase the instantaneous luminosity by a factor of five larger than the current levels achieved by the LHC. This high pile-up environment requires efficient and fast reconstruction of charged particles. A new algorithm called Line Segment Tracking takes a fundamentally different approach from existing iterative Kalman Filter based algorithms by doing a bottom-up reconstruction of tracks. Track stubs from adjoining detector regions are constructed, and stubs that are consistent with typical track trajectories are hierarchically linked to reconstruct complete tracks. Since the track stubs are produced locally and only require information from neighboring regions, they can be made in parallel. This motivates using architectures like GPUs to take advantage of the parallelism. The algorithm is currently implemented in the context of the CMS Phase-2 Outer Tracker in the HL-LHC, and targets NVIDIA Tesla V100 GPUs. Good physics and timing performance are obtained which paves way for further developments.