000002557 001__ 2557
000002557 005__ 20210720131558.0
000002557 037__ $$aBELLE2-MTHESIS-2021-072
000002557 041__ $$aeng
000002557 100__ $$aStella Katharina Wermuth
000002557 245__ $$aNeural Network based Pulse Shape Analysis with the Belle II Electromagnetic Calorimeter
000002557 260__ $$aHamburg$$bDESY$$c2021
000002557 300__ $$a89
000002557 500__ $$aPresented on 22 06 2021
000002557 502__ $$aMSc$$bHamburg, Universität Hamburg$$c2021
000002557 520__ $$aThe Belle II experiment, located at the SuperKEKB e+e- collider in Japan, uses pulse shape analysis techniques to distinguish electromagnetically and hadronically interacting particles within the CsI(Tl) electromagnetic calorimeter. The pulse shapes from the particle-dependent scintillation response are nominally analysed with a multi-template offline fit to measure the fraction of scintillation emission produced by hadrons. This fitting method allows for the determination of the total deposited energy, the total scintillation emission by hadrons, and the time of energy deposit. This thesis reports on a new approach to extract the total deposited energy, and the hadronic component of the scintillation emission from the pulse shapes using machine learning techniques. For this, a neural network is trained on pulse shapes produced in crystals from calorimeter clusters from simulated photons and pions, and is employed as a multivariate regression tool. Inferred on photons, the neural network outperforms the current fitting method in terms of crystal energy resolution and hadron intensity resolution. For pions the neural network shows a similar resolution compared with the current fitting method. Furthermore the neural network approach improves the discrimination of electromagnetic and hadronic interactions and is robust towards fluctuations in photon pile-up from beam backgrounds. Overall the neural network approach is promising, however additional fine tuning of the composition of the training sample could further improve its performance and robustness.
000002557 700__ $$aDr. Torben Ferber$$edir.
000002557 700__ $$aProf. Dr. Oliver Gerberding$$edir.
000002557 8560_ $$fstella.wermuth@desy.de
000002557 8564_ $$uhttps://docs.belle2.org/record/2557/files/BELLE2-MTHESIS-2021-072.pdf
000002557 980__ $$aTHESIS