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http://hdl.handle.net/2445/188641
Title: | Machine Learning Applied to High Energy Physics |
Author: | Costa Ledesma, Vanessa |
Director/Tutor: | Marín Benito, Carla |
Keywords: | Aprenentatge automàtic Xarxes neuronals (Informàtica) Treballs de fi de grau Machine learning Neural networks (Computer science) Bachelor's theses |
Issue Date: | Jun-2022 |
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. |
Note: | Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutora: Carla Marín Benito |
URI: | http://hdl.handle.net/2445/188641 |
Appears in Collections: | Treballs Finals de Grau (TFG) - Física |
Files in This Item:
File | Description | Size | Format | |
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COSTA LEDESMA VANESSA_6057605_assignsubmission_file_TFG-Costa-Ledesma-Vanessa.pdf | 529.32 kB | Adobe PDF | View/Open |
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