%0 Thesis
%A Vashishtha, Soumyaa
%T Search for long-lived supersymmetric decays in CMS using machine learning methods
%I University of Cologne
%V Masterarbeit
%M PUBDB-2025-05593
%P 66
%D 2025
%Z Masterarbeit, University of Cologne, 2025
%X As the search for physics beyond the Standard Model continues, this thesis presentsan analysis in the long-lived particle (LLP) supersymmetry sector. A Boosted DecisionTree (BDT) classifier was developed to enhance the search for long-lived supersymmetricpartner particles of the tau lepton (stau) particles in the muon-hadronic tau channel at theCMS detector in LHC, motivated by Gauge Mediated Supersymmetry Breaking (GMSB)scenarios. The signal region is characterized by the staus decaying to a muon and hadronictau. These displaced topologies were analyzed with the help of machine learning tools. Usingsimulated Run 2 CMS data, a BDT was constructed, including input feature selection, eventweighting, cross-validation, and model optimization. It demonstrates strong performance,achieving up to 90
%F PUB:(DE-HGF)19
%9 Master Thesis
%U https://bib-pubdb1.desy.de/record/642744