Searches for supersymmetric dark matter in semileptonic final states at the CMS experiment employing angular correlation and deep learning techniques followed by a reinterpretation in the pMSSM19 framework

The nature of dark matter (DM) remains one of the most compelling mysteries in modernphysics. Despite DM exceeding the visible (baryonic) matter by a factor of four, itsorigin and properties are yet to be understood. This thesis explores the dark matterproblem through the framework of supersymmetry (SUSY), a theoretical extension ofthe Standard Model of particle physics. Using data collected during the Large HadronCollider (LHC) Run 2 (2016-2018) by the Compact Muon Solenoid (CMS) experiment,with an integrated luminosity of L = 138 fb−1, multiple analysis strategies are employedto search for signatures of SUSY particles that present viable DM candidates.The first analysis utilizes a cut-and-count approach targeting the m˜g-m˜χ01 mass planevia angular correlation between selected physics objects, combined with a data-drivenmethod to address limitations in background modeling via transfer factors and correc-tions. This analysis achieved exclusion limits for gluino masses up to 2050 GeV andneutralino masses up to 1070 GeV. Subsequently, these results were reinterpreted withinthe phenomenological MSSM framework (pMSSM19), constraining additional SUSY pa-rameters and highlighting potential regions of interest based on observed data excesses.To further enhance the sensitivity, a machine learning-based approach was developed,utilizing a deep neural network (DNN) to classify collision events and define signal regionsbased on DNN scores. This novel methodology expands the exclusion limits up to 1450GeV for m˜χ01 and up to 2230 GeV for m˜g and demonstrates the advantages of sophisticatedcomputational techniques in modern collider analyses.Also, the HO, the outer hadron calorimeter of the CMS detector, was evaluated as apotential trigger system for long-lived particle (LLP) detection, addressing challenges inidentifying signatures predicted by SUSY theories.