000001600 001__ 1600
000001600 005__ 20190807093431.0
000001600 037__ $$aBELLE2-UTHESIS-2019-001
000001600 041__ $$aeng
000001600 100__ $$aStephanie Käs
000001600 245__ $$aMultiparameter Analysis of the Belle II Pixeldetector’s Data Using Principal Components Analysis and Neural Networks
000001600 260__ $$aGiessen$$bII. Physics Institute$$c2019
000001600 300__ $$a68
000001600 500__ $$aPresented on 01 07 2019
000001600 502__ $$aBSc$$bGiessen, Justus-Liebig-Universität$$c2019
000001600 520__ $$aIn a recent thesis, the use of self-organising maps (SOM) and feed forward networks for classification processes was applied in the analysis of data from Belle II’s pixel vertex detector. This thesis continues the work by examining correlations between the charge-related and size-related pixel cluster properties. To find correlations, principal components analysis was applied to a data set of antideuterons and beam background. Additionally, first attempts in the analysis of cluster shapes were made, observing that all clusters are either orientated in 45 ◦ direction or parallel to a module’s lateral borders. More than half of them can be grouped into single pixel clusters, rectangular, or quadratic shapes. The PCA-transformed data set was used to train a SOM. A comparison of the classification results to a SOM trained with the original data set lead to the result, that PCA does not necessarily improve the outcome. Lastly, use of a SOM was made to differentiate more than one particle from background, for the first time.
000001600 700__ $$aJens Sören Lange$$edir.
000001600 8560_ $$fjens.soeren.lange@desy.de
000001600 8564_ $$uhttps://docs.belle2.org/record/1600/files/BELLE2-UTHESIS-2019-001.pdf
000001600 980__ $$aTHESIS