Probing the dimuon channel of a Z' boson at the HL-LHC using multivariate analysis
The upcoming upgrade of the existing LHC facility at CERN is known as the High-Luminosity Large Hadron Collider (HL-LHC). It is designed to extend its physics reach by substantially increasing the integrated luminosity. It will enable more precise measurements of the Standard Model (SM) and improve sensitivity to rare events and possible new physics signatures. This study adopts a multivariate analysis (MVA) approach to effectively discriminate the dark Higgs (DH) signal against the dominant SM background. The analysis targets the leptonic decay mode of the $Z'$ boson, focusing on the dimuon final state at $\sqrt{s} = 14,\text{TeV}$ and $3000,\text{fb}^{-1}$ integrated luminosity corresponding to the HL-LHC. The DH signal is examined using the Toolkit for Multivariate Analysis (TMVA), employing and comparing the performance of three classifiers: Boosted Decision Trees (BDT), Deep Neural Networks (DNN), and Likelihood estimators.