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

dc.contributor.authorBeaubien, Alexandre
dc.contributor.supervisorRoney, J. Michael
dc.date.accessioned2021-12-23T23:33:26Z
dc.date.available2021-12-23T23:33:26Z
dc.date.copyright2021en_US
dc.date.issued2021-12-23
dc.degree.departmentDepartment of Physics and Astronomyen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThis 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).en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/13643
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectParticle Physicsen_US
dc.subjectMachine Learningen_US
dc.subjectBelle IIen_US
dc.subjectSimulationsen_US
dc.titleNoise waveform generation using GANs and charged particle identification using pulse shape discrimination in the belle II electromagnetic calorimeteren_US
dc.typeThesisen_US

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