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Domain Generalization with Machine Learning in the NOvA Experiment

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Éditeur :

Springer


Collection :

Springer Theses

Paru le : 2023-11-08

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Description

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 offalsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.
Pages
170 pages
Collection
Springer Theses
Parution
2023-11-08
Marque
Springer
EAN papier
9783031435829
EAN PDF
9783031435836
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
17
Taille du fichier
6636 Ko
Prix
158,24 €
EAN EPUB
9783031435836
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
17
Taille du fichier
22456 Ko
Prix
158,24 €

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