Using Advanced Machine Learning Techniques to Study Poorly Modeled Processes in pp Collisions with the ATLAS Detector
Using Advanced Machine Learning Techniques to Study Poorly Modeled Processes in pp Collisions with the ATLAS Detector
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DissertationDate of Exam
15.05.2023Date of Publication
12.07.2023Advisor
Brock, Ian C.Co-Referee
Bechtle, PhilipMetadata
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Abstract
In high energy particle physics, measurements are made to improve and test our models. Some processes are difficult or impossible to model with current capabilities. For some, this means that one would estimate such processes via data-driven techniques or use less-than-ideal modeling during the measurement. The aim of the research presented in this paper is to explore advanced machine learning techniques to deal with hard-to-model or unmodeled processes in analyses using the ATLAS detector. The two tackled problems addressed in this document are hadronically decaying tau-lepton identification and generating a sensitive variable for a WWbb measurement that enhances tW and ttbar interference.
Subjects
Maschinelles Lernen, Hochenergie-Teilchenphysik, Hintergrundmodellierung, Dichteabschätzung, ATLAS, machine learning, high energy particle physics, background modeling, density estimation
Classification (DDC)
530 PhysikDiaz Capriles, Federico Guillermo: Using Advanced Machine Learning Techniques to Study Poorly Modeled Processes in pp Collisions with the ATLAS Detector. - Bonn, 2023. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-70907
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-70907
@phdthesis{handle:20.500.11811/10937,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-70907,
author = {{Federico Guillermo Diaz Capriles}},
title = {Using Advanced Machine Learning Techniques to Study Poorly Modeled Processes in pp Collisions with the ATLAS Detector},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2023,
month = jul,
note = {In high energy particle physics, measurements are made to improve and test our models. Some processes are difficult or impossible to model with current capabilities. For some, this means that one would estimate such processes via data-driven techniques or use less-than-ideal modeling during the measurement. The aim of the research presented in this paper is to explore advanced machine learning techniques to deal with hard-to-model or unmodeled processes in analyses using the ATLAS detector. The two tackled problems addressed in this document are hadronically decaying tau-lepton identification and generating a sensitive variable for a WWbb measurement that enhances tW and ttbar interference.},
url = {https://hdl.handle.net/20.500.11811/10937}
}
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-70907,
author = {{Federico Guillermo Diaz Capriles}},
title = {Using Advanced Machine Learning Techniques to Study Poorly Modeled Processes in pp Collisions with the ATLAS Detector},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2023,
month = jul,
note = {In high energy particle physics, measurements are made to improve and test our models. Some processes are difficult or impossible to model with current capabilities. For some, this means that one would estimate such processes via data-driven techniques or use less-than-ideal modeling during the measurement. The aim of the research presented in this paper is to explore advanced machine learning techniques to deal with hard-to-model or unmodeled processes in analyses using the ATLAS detector. The two tackled problems addressed in this document are hadronically decaying tau-lepton identification and generating a sensitive variable for a WWbb measurement that enhances tW and ttbar interference.},
url = {https://hdl.handle.net/20.500.11811/10937}
}
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