Domain generalization with machine learning in the NOvA experiment / Andrew T.C. Sutton.
2023
QC793.5.N42
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Title
Domain generalization with machine learning in the NOvA experiment / Andrew T.C. Sutton.
ISBN
9783031435836 (electronic bk.)
3031435834 (electronic bk.)
9783031435829
3031435826
3031435834 (electronic bk.)
9783031435829
3031435826
Published
Cham : Springer, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xi, 170 pages) : illustrations (chiefly color).
Item Number
10.1007/978-3-031-43583-6 doi
Call Number
QC793.5.N42
Dewey Decimal Classification
539.7/215
Summary
This thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.
Note
"Doctoral thesis accepted by the University of Virginia, USA."
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 15, 2023).
Series
Springer theses. 2190-5061
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Domain Generalization with Machine Learning in the NOvA Experiment
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Table of Contents
Chapter 1: Neutrinos: A Desperate Remedy
Chapter 2. A Review of Neutrino Physics
Chapter 3. The NOvA Experiment
Chapter 4. Event Reconstruction
Chapter 5. The 3-Flavor Analysis
Chapter 6. A Long Short-Term Memory Neural Network
Chapter 7. Domain Generalization by Adversarial Training
Chapter 8. Conclusion.
Chapter 2. A Review of Neutrino Physics
Chapter 3. The NOvA Experiment
Chapter 4. Event Reconstruction
Chapter 5. The 3-Flavor Analysis
Chapter 6. A Long Short-Term Memory Neural Network
Chapter 7. Domain Generalization by Adversarial Training
Chapter 8. Conclusion.