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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/188641
Machine Learning Applied to High Energy Physics
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Abstract
Machine learning algorithms have gained traction in a variety of fields throughout the last decade. This final degree project focuses on a bank problem and on a high-energy physics problem: searching for a rare Λ0b
decay. Two different machine learning methods are used: Neural Networks and Boosted Trees, implemented in three different Phython libraries: TensorFlow and Keras, PyTorch and XGBoost. Using the AUC-ROC curve, the models between the three libraries are compared, and finally, models try to predict whether the Λ0b
decay happens for a given data.
Results for the bank problem shows nearly the same performance for TensorFlow and PyTorch, while XGBoost seems significantly better. For the high-energy problem XGBoost seems better, followed by TensorFlow and last PyTorch. However, predictions made on new data shows similar performance for XGBoost and PyTorch.
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Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutora: Carla Marín Benito
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COSTA LEDESMA, Vanessa. Machine Learning Applied to High Energy Physics. [consulted: 11 of June of 2026]. Available at: https://hdl.handle.net/2445/188641