UC Irvine

This thesis covers a new search for dark matter at the LHC in addition to improved particle identification techniques using neural networks. Models are introduced for a new search at the LHC, mono-Z', where dark matter is produced in association with a new hypothetical boson. Searches are performed using data collected from p p collisions at 13 TeV using the ATLAS detector. Measurements from this search are consistent with the Standard Model, and upper limits are estimated on the production cross sections and model parameters. Next, we explore techniques for identification of for jets and muons using deep convolutional neural networks trained on calorimeter energy deposits. In both studies, improvements on the baseline techniques using high-level engineered features is observed.