Abstract
Dark matter is one of the great mysteries of modern physics. Despite a substantial body of evidence pointing to the existence of some kind of gravitationally interacting, mass bearing particle-like substance, it remains as yet unclear what the true nature of this mysterious matter might be. One potential candidate for this dark matter is the WIMP (Weakly Interacting Massive Particle). Presented in this thesis are three contributions to the LZ (LUX-ZEPLIN) dark matter direct detection experiment, which seeks to detect WIMPs by their scattering of Xenon nuclei. The first of these is development and application of software for monitoring in detail changes in activity in LZ's OD (outer detector), a critical part of LZ's neutron veto system. This software is able to quantify in previously unavailable ways the effect of changes in detector conditions and the impact of upgrades and modifications to the LZ systems quickly, providing rapid actionable information to LZ's operations.The second body of work is that of a machine learning approach to the counting of photons in the LZ detector, able to more accurately count the photons in simulations of LZ than the current generation software in use. This approach uses a combination of a convolutional neural network with a multiclassifier to quantify the number of photons in waveforms from a single PMT (photomultiplier tube), even in the case of pileup. This results in a 99% of photons found relative to 95% in LZ's existing software in simulated data.
The third and final contribution to LZ presented is an analysis of the data from LZ's SR1 (Science Run 1) using a Lagrangian effective field theory approach to achieve world leading results for the limits on the coupling strengths these lagrangians. This study is conducted to an expanded energy region of interest, increasing from 70 keV nuclear recoil energies in LZ's initial WIMP search to 350 keV.
| Date of Award | 7 May 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Jim Brooke (Supervisor) & Henning U Flaecher (Supervisor) |