Background Measurement Techniques and Sensitivity Studies for the SNO+ Neutrinoless Double Beta Decay Search

The control of radioactive backgrounds will be key in the search for neutrinoless double beta decay at the SNO+ experiment. Several aspects of the SNO+ back- grounds have been studied. The SNO+ tellurium purification process may require ultra low background ethanol as a reagent. A low background assay technique for ethanol was developed and used to identify a source of ethanol with measured 238U and 232Th concentrations below 2.8 10^-13 g/g and 10^-14 g/g respectively. It was also determined that at least 99:997% of the ethanol can be removed from the purified tellurium using forced air ow in order to reduce 14C contamination. In addition, a quality-control technique using an oxygen sensor was studied to monitor 222Rn contamination due to air leaking into the SNO+ scintillator during transport. The expected sensitivity of the technique is 0.1mBq/L or better depending on the oxygen sensor used. Finally, the dependence of SNO+ neutrinoless double beta decay sensitivity on internal background levels was studied using Monte Carlo simulation. The half-life limit to neutrinoless double beta decay of 130Te after 3 years of operation was found to be 4.8 1025 years under default conditions.

The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.