| Title: |
Optimizing Neutrino Flavor Conversion Measurements through Machine Learning |
| Authors: |
Yankelevich, Alejandro Jaime |
| Contributors: |
Bian, Jianming; Sobel, Henry |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2026 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
Particle physics; Nuclear physics; Physics |
| Description: |
The phenomenon of neutrino flavor conversion whereby the flavor of a neutrino particle can change between its time of production and later detection was the first definitive evidence of physics beyond the Standard Model. Some of the oscillation parameters used to describe this conversion are not yet well measured, leaving important questions still open regarding flavor conversion both in vacuum and as neutrinos travel through matter. NOvA is a long-baseline neutrino oscillation experiment that uses Fermilab’s predominantly νµ NuMI beam. A 14 kton oil-based liquid scintillator far detector 810 km away is used to measure neutrino oscillation through the νµ disappearance and νe appearance channels. Super-K is a 50 kton water Cherenkov detector, which measures the disappearance of νe produced during solar fusion. The high density environment of the sun decreases the νe survival probability at higher energies observable in Super-K compared to the vacuum-dominated oscillations at lower energies. However, the transition region is overshadowed by radioactive background in the detector. In both of these experiments, the separation of neutrino detection events from background and classification of neutrino flavor are crucial tasks that benefit from the introduction of machine learning. Chapter 1 gives an overview of neutrinos and the context under which flavor conversion is measured in this dissertation. Chapters 2–7 present the results of a Bayesian sampling approach for the latest NOvA 3-flavor oscillation analysis with 26.6 × 1020 protons on target in neutrino mode and 12.5 × 1020 in antineutrino mode collected over 10 years. Chapters 8–13 present the results of extending the Super-K solar analysis to lower energies during its fourth phase with 2970 days of livetime. |
| Document Type: |
thesis |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt5gh7z496; https://escholarship.org/uc/item/5gh7z496; https://escholarship.org/content/qt5gh7z496/qt5gh7z496.pdf |
| Availability: |
https://escholarship.org/uc/item/5gh7z496; https://escholarship.org/content/qt5gh7z496/qt5gh7z496.pdf |
| Rights: |
public |
| Accession Number: |
edsbas.B0B2B5A7 |
| Database: |
BASE |