Search for Dark Matter using Machine Learning in dilepton and missing energy events with the ATLAS detector at the LHC

In this master thesis, we compared the performance of a Neural Network (NN) and a Boosted Decision Tree (BDT) Machine Learning (ML) algorithm for the binary classification of Dark Matter (DM) signal and the Standard Model (SM) background. We conducted searches based on models from different theoretical principles that share common experimental signatures, including three models based on a new $Z'$ vector boson coupled to a DM candidate: the Dark Higgs model, Light Vector model, and an inelastic Effective Field Theory model. We also studied the direct slepton production model in Supersymmetry, and a Two Higgs Doublet Model with an additional pseudoscalar mediator. In the process of networks optimization we explored methods to mitigate the phenomena of missing variables on datasets, as well as how to weigh simulated samples that have negative weights. Our study involved two approaches: a model dependent approach training one BDT for each model and a model independent approach training three BDTs, in kinematically orthogonal regions for all models simultaneously. We demonstrated that the ML model independent approach consistently achieved higher mass exclusion limits for all studied models compared to the model dependent approach. To perform these analyses, proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider during the Run II data taking period (2015-2018), corresponding to an integrated luminosity of 139 fb$^{-1}$, was used. The utilization of the ATLAS data provided a crucial foundation for training and evaluating the ML algorithms, ensuring their relevance and applicability to real-world physics phenomena.