Noise waveform generation using GANs and charged particle identification using pulse shape discrimination in the belle II electromagnetic calorimeter




Beaubien, Alexandre

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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 decision 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± discrimination. Results also show an improvement to the result obtained with the currently implemented PSD discriminator trained on neutral particles (γ, K-long).



Particle Physics, Machine Learning, Belle II, Simulations