Combining T2K with other experiments to better constrain oscillation parameters

Goodman, Evan Arthur Gerald (2024) Combining T2K with other experiments to better constrain oscillation parameters. PhD thesis, University of Glasgow.

Abstract

This thesis presents an analysis of T2K data using a new external reactor constraint from Daya Bay instead of the regular one-dimensional Gaussian provided by the Particle Data Group (PDG). Both the PDG and Daya Bay data sets can be used to update the prior of given parameters in the T2K analyses. Applying Daya Bay’s two-dimensional constraint on the mixing angle θ₁₃ and mass splitting Δm² ₃₂ improves the constraint on the mass splitting parameter by 25% in normal hierarchy and 18% in inverted hierarchy compared to using the PDG external prior. Furthermore, denoted with a Bayes factor value which compares two hypotheses using the posterior results, it was found that there was a small increase in the preference for normal hierarchy over inverted hierarchy, B(NH/IH): PDG = 2.77 and Daya Bay = 2.79. There was a slightly larger increase for the upper octant in the octant degeneracy, B(UO/LO): PDG = 2.27 and Daya Bay = 2.38. The thesis also describes development work towards the first full joint-fit between two long baseline experiments, T2K and NOvA, showcasing the increase in statistical sensitivity for the oscillation parameters and the potential to solve some of the current degeneracies limiting the sensitivity of both experiments. Finally, there is an introductory insight into an alternate parameterisation of neutrino oscillations that could be used to better understand the constraint from the T2K data.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Litchfield, Dr. Phillip
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84120
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 05 Mar 2024 11:31
• Last Modified: 05 Mar 2024 11:32
• Thesis DOI: 10.5525/gla.thesis.84120
• URI: https://theses.gla.ac.uk/id/eprint/84120