Noise Waveform Generation Using GANs and Charged Particle Identification Using Pulse Shape Discrimination in the Belle II Electromagnetic Calorimeter
Category: Master Thesis, Visibility: Public
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Authors | Alexandre Beaubien, John Roney |
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Date | 2021-01-01 |
Belle II Number | BELLE2-MTHESIS-2022-010 |
Abstract | This 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). |
Conference | Victoria |
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BELLE2-MTHESIS-2022-010.pdf (versions: 1)
latest upload: 2024-12-02