TY  - THES
AU  - Vashishtha, Soumyaa
TI  - Search for long-lived supersymmetric decays in CMS using machine learning methods
PB  - University of Cologne
VL  - Masterarbeit
M1  - PUBDB-2025-05593
SP  - 66
PY  - 2025
N1  - Masterarbeit, University of Cologne, 2025
AB  - 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
LB  - PUB:(DE-HGF)19
UR  - https://bib-pubdb1.desy.de/record/642744
ER  -