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Durham e-Theses
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Deep Learning Applications in Flavour Tagging

KWOK, KA,WANG (2022) Deep Learning Applications in Flavour Tagging. Doctoral thesis, Durham University.

Full text not available from this repository.
Author-imposed embargo until 06 May 2025.

Abstract

Motivated by the application of data-driven solutions to the field of particle physics, in particular flavour tagging, we study the effectiveness of deep learning (DL) approaches for inclusive \ensuremath{|V_{ub}|}\xspace measurement within the Belle II environment and strangness tagging in the LHCb environment.\\
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In the \ensuremath{|V_{ub}|}\xspace study, we compare the performance of an existing Boosted Decision Tree approach with a Bayesian neural network. In addition, we perform an in depth study on the selected features, investigating the signal inclusivity of DL models which gives insights into behaviours of the models.\\
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We aim for classification speed and precision in the strange-quark jets tagging study. Therefore, we explore using a simple fully connected feedforward neural network to classify $s$-jets among all light jet backgrounds. A comprehensive feature investigation is performed to understand the discriminating power of jet observable $J_s$ and the importance of particle identification.\\
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Additionally, data-driven methodologies are also reshaping industrial practices. A study investigating the potential of DL in predicting realised volatility of a financial index is included. It is a collaborative project with Optiver where neural networks along with various training schemes are studied to maximise profits.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Faculty and Department:Faculty of Science > Physics, Department of
Thesis Date:2022
Copyright:Copyright of this thesis is held by the author
Deposited On:09 May 2022 10:33

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