Possible Theses Topics (Data Analysis)
Beyond Standard Model PhysicsCopyright: CMS Collaboration/CERN
The CMS experiment at the LHC accelerator is especially designed for the discovery of new particles or phenomena. From 2015-2018, the LHC Run II ran with a proton-proton centre-of-mass energy of 13 TeV. The recorded data will be evaluated in various bachelor theses. During the work, methods and contents of current experimental particle physics as well as the handling of more or less complex program packages (C++, Python) will be learned. An interest in working with computers is expected.
SUSY and lepton-number violating resonances
Supersymmetry is one of the best studied candidates for physics beyond the Standard Model. In our group, we are looking for resonant generation of supersymmetric particles in R-Parity Violating (RPV) scenarios that leave an easily detectable signature in the detector (electron-muon, electron-tau, or muon-tau). Such signatures can also occur in other theories besides RPV SUSY and represent a clear signal. The goals of the bachelor thesis are important contributions to the progress of the analysis of the existing 13 TeV data. There are also possibilities, based on previous studies, to test the data for signs of other exotic phenomena, such as black holes and sphalerons. Another possible bachelor thesis in collaboration with this analysis concerns the measurement of standard model parameters.
Model-independent seach for new physics (MUSiC)Copyright: CMS Collaboration
The MUSiC analysis (Model Unspecific Search in CMS) enables a model-independent search for new phenomena in CMS with minimal assumptions about the underlying models. The MUSiC analysis is the only analysis of its kind in CMS and receives special attention. Selected collision events are divided into different classes based on their final state. In each class, some sensitive distributions are analyzed and the respective significance is determined. In the work, one or more new event classes are to be analyzed in the CMS data to search for discrepancies with the predictions of the Standard Model.