000003140 001__ 3140
000003140 005__ 20220802202446.0
000003140 037__ $$aBELLE2-MTHESIS-2022-010
000003140 041__ $$aeng
000003140 100__ $$aAlexandre Beaubien
000003140 245__ $$aNoise Waveform Generation Using GANs and Charged Particle Identification Using Pulse Shape Discrimination in the Belle II Electromagnetic Calorimeter
000003140 260__ $$aVictoria$$bUniversity of Victoria$$c2021
000003140 300__ $$amult. p
000003140 500__ $$aPresented on 17 12 2021
000003140 502__ $$aMSc$$bVictoria, University of Victoria$$c2021
000003140 520__ $$aThis thesis investigates the use of generative adversarial networks (GANs) as an alternative method to simulate noise waveforms for Belle II CsI(Tl) calorimeter crystals. Presented is a deep convolutional GAN (DCGAN) trained using background waveforms recorded in the ECL during a physics run. Results are presented showing good agreement in the distribution of metrics comparing data and simulated noise waveforms using a two-sample Kolmogorov-Smirnov test. The models are shown to be difficult to train, and many possible improvements are identified. Secondly, this thesis showcases the development of a particle identification tool relying on pulse shape discrimination (PSD) as an input to a gradient boosted de- cision tree (GBDT) classifier. Two models are trained to discriminate μ±, π± and e±, π±. Results show that PSD charged particle identification in the ECL improves the e±, π± discrimination, but result in smaller improvements to the μ±, π± discrim- ination. Results also show an improvement to the result obtained with the currently implemented PSD discriminator trained on neutral particles (γ, K0 L).
000003140 700__ $$aJohn Michael Roney$$edir.
000003140 8560_ $$falexandrebeaubien@uvic.ca
000003140 8564_ $$uhttps://docs.belle2.org/record/3140/files/BELLE2-MTHESIS-2022-010.pdf
000003140 980__ $$aTHESIS