Department of Physics, Engineering Physics and Astronomy Graduate Theses

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    Investigating and Characterizing Electrical Resistivity Variations Caused by Heat Treatment in Zr2.5%Nb Pressure Tubes
    (2024-07-09) Thorpe, William; Physics, Engineering Physics and Astronomy; Krause, Thomas
    The electrical resistivity variations in the circumferential and radial directions in pressure tubes (PTs) at various heat treatment (HT) levels, encompassing the range of as-manufactured HT times, were characterized as these have consequences for PT-calandria tube (CT) gap measurement error in as-installed PTs. The methods employed included a combination of eddy current (EC) testing (ECT) for resistivity measurements, and scanning electron microscopy (SEM) for microstructural measurements of β_Zr ribbon thickness. A full factorial experiment was performed on the average circumferential resistivity in PT samples taken from both extrusion ends, with various HT levels, EC frequencies and probe surface placements (inner or outer PT surface). The resistivity measurements using an EC frequency of 1500 kHz and β_Zr ribbon thickness measurements in the radial direction showed a weak parabolic variation that was correlated with β_Zr ribbon thickness measurements in the radial direction. The SEM measurements showed a statistically significant difference in average β_Zr ribbon thickness of 150 nm between the outer and inner surfaces in the axial-transverse cross-section, which may explain the statistical significance of measured inner and outer surface resistivity variations in some PT samples. The multivariate analysis of variance (MANOVA) showed that the combination of HT and EC frequency was the second most significant test factor combination that accounted for about 30% of the total variance in the data, which is evidence of radial resistivity variations being created by the HT process. Measurements of resistivity in the circumferential direction showed variation of up to ± 1.25% and 1.5% of a PT’s average resistivity from the inner and outer surfaces, respectively, which may have implications for PT-CT gap measurement accuracy using analytic-based inverse algorithms that do not compensate for circumferential resistivity variations. Results obtained from mulit-frequency ECT in the circumferential direction across multiple PT samples showed that HT causes the average PT resistivity to decrease at a rate of 1.53±0.08 (μΩ⋅cm)/log⁡(hr) and 1.1±0.4 (μΩ⋅cm)/log⁡(hr) for the inner and outer PT surfaces, respectively. These results are correlated with differences in average β_Zr ribbon thickness in the axial-transverse cross-section and provide further evidence of a radial resistivity variation being created due to HT.
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    Design and Optimization of Inverse Phononic Crystals in Gallium Arsenide for Enhanced Surface Acoustic Waveguiding
    (2024-07-03) Singh, Karanpreet; Physics, Engineering Physics and Astronomy; Stotz, James
    This research designs and optimizes inverse phononic crystal waveguides for efficient surface acoustic wave (SAW) propagation in Gallium Arsenide. Traditional SAW waveguides often suffer from limitations such as beam spreading and energy leakage into the bulk substrate. This work introduces periodic inclusions into the substrate to reduce the SAW eigenfrequency modes below the bulk shear horizontal mode. A comprehensive finite element method simulation framework is established to analyze the band structure and displacement fields of various phononic crystal designs, employing rigorous mesh and boundary sensitivity analyses to ensure result accuracy. While initial optimization studies on cylindrical inclusions reveal limitations in achieving strong confinement, exploration of alternative geometries, including ellipsoids and elliptical cylinders, demonstrate significant improvements. The research culminates in the design of a four-inclusion wide waveguide using elliptical cylinders, which achieves logarithmic reciprocal attenuation ten orders of magnitude better than prior designs indicating extremely strong confinement to the surface and minimal energy leakage into the bulk. This innovative waveguide design promises to enhance a wide range of applications that require efficient on-chip SAW manipulation.
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    Background Characterization of NEWS-G3 for Detecting Coherent Elastic Neutrino-Nucleus Scattering at a Nuclear Reactor
    (2024-06-27) Meadows, Hayden James; Physics, Engineering Physics and Astronomy; Giroux, Guillaume
    The NEWS-G collaboration uses spherical proportional counters (SPCs) to perform a direct detection dark matter search for weakly interacting massive particles (WIMPs). SPCs exploit the ionization of gas to detect nuclear recoils at sub-keV sensitivities, which makes them excellent candidates for the detection of coherent elastic neutrino-nucleus scattering (CEvNS) using a nuclear reactor as a neutrino source. One of the greatest challenges in detecting CEvNS is overcoming the overwhelming backgrounds that obscure the elusive signals. NEWS-G3 is a new experiment using an SPC to detect CEvNS at a nuclear reactor. To suppress the background effects, NEWS-G3 has seven layers of passive shielding of varying materials and a single layer of an active muon veto scintillator system. The muon veto scintillator system has been successfully characterized and optimally calibrated and, through simulation, is shown to have an efficiency ≥99.85%. Simulation results explore the effects on overall muon-induced background rates for different veto window lengths and target gas parameters. First measurements using the NEWS-G3 detector result in a ≥98.5% background suppression, with future optimization to occur. The work presented in this thesis demonstrates an understanding of the expected background rates and highlights the feasibility of performing a CEvNS search using NEWS-G3 at a nuclear reactor.
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    Effect of Dark Radiation on Neff in Cosmology with Heavy Particle Decay or with Dark Matter Composites
    (2024-06-19) Bleau, Katarina Justine; Physics, Engineering Physics and Astronomy; Bramante, Joseph
    Any light relic which was in thermal equilibrium with the Standard Model before it freezes out results in a shift in the effective number of neutrino species, Neff. Here, we focus on the impact of two different cosmologies involving light particles on this quantity. First, we explore how observational bounds on Neff are loosened if the energy density of the light particles is diluted with respect to that of Standard Model radiation. This can happen if a heavy particle that is decoupled from the Standard Model decays into the Standard Model bath after the light particle freezes out. After calculating how heavy state decays alter Neff for light particles beyond the Standard Model, we focus in particular on the case that the heavy decaying particle is a gravitino, and use current bounds on Neff to place constraints on the gravitino mass and the branching ratio into light particles for different values of the reheating temperature of the Universe. Second, we shift gears towards a cosmology where dark matter is comprised of fermionic bound states, whose interactions are mediated by a light scalar field. In this case, we use current limits on Neff to constrain the dark matter mass and its coupling to the light scalar for different light scalar masses.
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    Event Reconstruction in SNO+ with Graph Neural Networks
    (2024-06-05) Cheng, Sabrina Zhongliang; Physics, Engineering Physics and Astronomy; Wright, Alexander; Martin, Ryan
    SNO+ is a large-scale neutrino experiment with the primary goal of searching for neutrinoless double beta decay in Te-130. The reconstruction of the position of particle interaction events in the detector volume is central to the SNO+ data analysis pipeline. This thesis presents a new method of position reconstruction using the machine learning method of Graph Neural Networks (GNN). Based on a simulated dataset of events in the energy range of 0.5 to 5 MeV, a comparison is made between the GNN, the current reconstruction method, and an independent convolutional neural network method developed within the SNO+ collaboration. The GNN reduces the average fit error by 13.1% over the current reconstruction method, and by 12.8% over the convolutional neural network method. In addition, the GNN reduces the percentage of events misplaced outside of the detector from 6% to 2% compared to the current method of reconstruction. A separate GNN is trained to reconstruct energy using the same architecture as the position reconstruction model to test the ability of the GNN to generalize to other applications. Under these conditions, GNN improves the average fit error by 16.8% over the current method of energy reconstruction. When operating on a CPU, the GNN has a comparable inference speed to the current reconstruction method, and when operating on a GPU, the GNN's inference speed is 96.7% faster.