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.
| ISBN: | 9783031435850 |
| Publication date: | 9th November 2024 |
| Author: | Andrew T C Sutton |
| Publisher: | Springer an imprint of Springer Nature Switzerland |
| Format: | Paperback |
| Pagination: | 170 pages |
| Series: | Springer Theses |
| Genres: |
Particle and high-energy physics Machine learning Scientific standards, measurement etc Mathematical physics |
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.
Domain Generalization With Machine Learning in the NOvA Experiment features in the following genres: Particle and high-energy physics, Machine learning, Scientific standards, measurement etc, Mathematical physics
Domain Generalization With Machine Learning in the NOvA Experiment is available in Paperback
Domain Generalization With Machine Learning in the NOvA Experiment was written by Andrew T C Sutton and published by Springer an imprint of Springer Nature Switzerland
Domain Generalization With Machine Learning in the NOvA Experiment has 170 pages
Yes it is part of Springer Theses series
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