Optimization of tbH$^+$ Signal and Background Separation Using Machine Learning in the 2lSS1tau Channel, Comparison of Limit Setting Techniques and Signal Injection Studies - Summer Student Report

Machine Learning techniques have proven the potential to improve the separation of signal and background events in High Energy Physics (HEP) data. This study focuses on the optimization of the tbH$^+$ search sensitivity in the analysis channel with two same electrically charged light leptons and one hadronically decaying tau. The analysis is performed with simulated data and the results are expressed as expected sensitivity including statistical uncertainties. In addition, the consistency of different limit setting techniques is demonstrated, and signal injection studies are performed.