Noise waveform generation using GANs and charged particle identification using pulse shape discrimination in the belle II electromagnetic calorimeter
| dc.contributor.author | Beaubien, Alexandre | |
| dc.contributor.supervisor | Roney, J. Michael | |
| dc.date.accessioned | 2021-12-23T23:33:26Z | |
| dc.date.available | 2021-12-23T23:33:26Z | |
| dc.date.copyright | 2021 | en_US |
| dc.date.issued | 2021-12-23 | |
| dc.degree.department | Department of Physics and Astronomy | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.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). | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13643 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Particle Physics | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Belle II | en_US |
| dc.subject | Simulations | en_US |
| dc.title | Noise waveform generation using GANs and charged particle identification using pulse shape discrimination in the belle II electromagnetic calorimeter | en_US |
| dc.type | Thesis | en_US |