16N calibration and background studies for SNO+

SNO+ is a multipurpose scintillator based neutrino experiment which is located 2km underground at SNOLAB, Creighton mine, Sudbury. The primary physics goal of the experiment is the search for the elusive process of neutrino-less double beta decay with 130Te loaded into the liquid scintillator cocktail. In addition, SNO+ will be able to detect low energy solar neutrinos, geo- and reactor-antineutrinos, as well as supernova neutrinos. SNO+ has completed its water phase in 2019. During the water phase SNO+ made measurements of 8B neutrinos, and improved the limits on the lifetime of nucleon decay. Aside from the physics goals, the optical and energy calibration of the detector was accomplished in water phase. SNO+ has finished filling the detector in May 2022, and preparing for the 130Te phase. This thesis consists of author’s major contributions to the experiment: i) calibration analysis described in chapter 7, and ii) the background analyses discussed in chapter 6 and chapter 7. Chapter 5 focuses on the calibration of SNO+ detector using 16N calibration source. The tagged 6.1 MeV γ’s from 16N provided the primary energy calibration data in the water phase. Furthermore, the source was deployed externally throughout the scintillator filling process, and the data was used for various calibration purposes such as studying the scintillation light yield, verifying the reconstruction algorithms, characterizing the scintillation timing, and studying the Cherenkov signal in liquid scintillator. The second part of my analysis focuses on background analyses in SNO+. Chapter 6 describes a model that I have used to estimate the effective attenuation length of the detector in scintillator phase. The model was used to fit the tagged 214Po events in scintillator phase, and estimate the effective attenuation length. The result is consistent with other studies. Moreover, chapter 7 focuses on the external backgrounds. A set of timing and angular classifiers have been developed, and originally optimised to distinguish the external backgrounds from 0νββ signal. I have utilised these classifiers to investigate their performance for the detectable solar ν signals. The classifiers found effective, however it is demonstrated that the performance can be significantly improved by taking advantage of supervised learning methods. ROOT TMVA was used for this classification study. Furthermore, I have used the partial-fill scintillator data to estimate the level of the external backgrounds. The 2.6 MeV γ signal from the external 208Tl is identified in partial-fill. Furthermore, the level of external 208Tl γ’s from the hold-down ropes are estimated by taking advantage of their angular symmetry. The estimated result is consistent with previous measurements taken in water phase. Moreover, I have used the vertical displacements of the AV to estimate the creep rate of the hold-down rope system during the partial-fill period. This analysis is described in appendix A. Furthermore, the long-term stability tests of the Tensylon fibres are described in the second part of appendix A. Finally, the leaching model is briefly described in appendix B. I have developed this model as part of my MSc. research. During my first year of Ph.D, I had the chance to complete the model and develop a simple python tool to estimate the surface activity, and the level of leached isotopes for different filling scenarios.

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