Noise waveform generation using GANs and charged particle identification using pulse shape discrimination in the belle II electromagnetic calorimeter
Date
2021-12-23
Authors
Beaubien, Alexandre
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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 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).
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Keywords
Particle Physics, Machine Learning, Belle II, Simulations