Kinematic Study of 𝑯^𝟎→𝒈𝒈→𝒃𝒃̅ 𝒃𝒃̅ and other background processes

Lead Undergraduate Research Assistant to Dr. Stephen Sekula

• Performed a data analysis study on truth-particle simulated kinematic information from POWHEG+PYTHIA8 to study high momentum Higgs events
• Used Python programming language to analyze kinematic information and visually represented frequency distributions of variables from different Higgs processes
• Applied machine learning algorithms, using the Python library class scikit-learn, to classification of Higgs processes using kinematic information of truth-particles
• Constructed detailed report of research project on the Kinematic Study of 𝐻⁰→𝑔𝑔→𝑏𝑏̅ 𝑏𝑏̅ and other background processes that is documented as an official internal note on the CERN Document Server (Record #2676102) as an ATLAS Project
• Research project was funded by the US ATLAS SUPER Project Research Grant, the SMU Hamilton Undergraduate Research Scholars Program, and the SMU Undergraduate Research Assistanceship (URA) Research Grant