Applications of Deep Neural Networks in a Top Quark Mass Measurement at the LHC

In this analysis the usage of deep neural networks for an improved event selection forthe top-quark-mass measurement in the t¯ muon+jets channel for events at the CMS ext√periment for the LHC run II with a center of mass energy s = 13 TeV was investigated.The composition of the event selection with respect to different jet-assignment permutationtypes was found to have a strong influence on the systematic uncertainty of the top-quarkmass measurement. A selection based on the output of neural network trained on classifyingevent permutations of the t¯ muon+jets final state into these permutation types could thentbe used to improve the systematical uncertainty of the current mass measurement from asystematical uncertainty of around 630 MeV to 560 MeV.