000001047 001__ 1047
000001047 005__ 20180723084817.0
000001047 037__ $$aBELLE2-UTHESIS-2018-002
000001047 041__ $$aeng
000001047 100__ $$aGordian Edenhofer
000001047 245__ $$aOptimization of Particle Identification
000001047 260__ $$aMünchen$$bLudwig-Maximilians-Universität$$c2018
000001047 300__ $$a57
000001047 500__ $$aPresented on 29 06 2018
000001047 502__ $$aBSc$$bMünchen, Ludwig-Maximilians-Universität$$c2018
000001047 520__ $$aThis study aims at evaluating particle identification approaches. First, the goodness of the detector yield is measured. Flaws are revealed and possible causes evaluated. In addition, current and future techniques for combining detector variables are outlined. Next, a Bayesian approach to particle identification is discussed. It aims to produce probabilities of a track belonging to a particle species depending on the received signals. The process of obtaining conditional probabilities is described in detail. Furthermore, some extensions to the Bayesian approach are presented and evaluated. Flaws and benefits are compared using a generic decay. Finally, a neural network is used to label particle tracks. Different methods to adapt the weights and various pre-processing steps are evaluated for a simple network. Hereby, tools from machine learning and statistics are discussed and their application is outlined. Last but not least, the accuracy of the network on a generic decay is determined and a comparison with the Bayesian approaches is performed.
000001047 700__ $$aThomas Kuhr$$edir.
000001047 8560_ $$fthomas.kuhr@lmu.de
000001047 8564_ $$uhttps://docs.belle2.org/record/1047/files/BELLE2-UTHESIS-2018-002.pdf
000001047 980__ $$aTHESIS