Date Available
11-9-2025
Year of Publication
2025
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Arts and Sciences
Department/School/Program
Physics and Astronomy
Faculty
Ryan MacLellan
Faculty
Anatoly Dymarsky
Abstract
The proposed next Enriched Xenon Observatory (nEXO) experiment aims to detect neutrinoless double beta decay in 136Xe, a rare process with profound implications for particle physics and our understanding of the neutrino. Achieving the required sensitivity demands ultra-low background conditions, making both material radiop urity screening and precise detector calibration essential. This dissertation addresses two key calibration challenges in preparation for nEXO. First, I investigated discrepancies in calibration efficiency measurements of the high-purity germanium (HPGe) detector named as GeIV located 4850 feet under ground at the Sanford Underground Research Facility, Lead, SD. The root cause for the discrepancy was found to be the density of the bead that contains a radioactive source. I developed a toy Monte Carlo framework to infer the physical bead den sity, enabling accurate modeling of the detector response and precise determination of GeIV’s dead layer thickness. These refinements significantly improved the agree ment between measurement and simulation and enhanced the reliability of material screening for nEXO. Second, I developed a robust simulation framework to optimize the external gamma-ray calibration system for the proposed full-scale nEXO detector. Using GEANT4-based Monte Carlo simulations, I studied the calibration performance of 228Th, 226Ra, and 60Co sources under various operational conditions. A central out come of this work is the optimization of source strength: thereby preserving detector live time. This approach offers flexibility in isotope selection and calibration plan ning, and expands significantly on preliminary estimates provided in the experiment’s pre-Conceptual Design Report. Additionally, I evaluated the performance of the current deep neural network (DNN)models used for event classification and found reduced accuracy for low-energy gamma sources. I propose model retraining and alternative classification strategies to overcome these limitations. Together, these contributions provide critical tools and insights for designing an effective calibration system in nEXO, ensuring the experiment’s background goals are met and supporting its overall sensitivity to rare-event searches.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2025.54
Recommended Citation
Bhatta, Thakur P., "OPTIMIZING EXTERNAL RADIOACTIVE CALIBRATION SOURCE STRENGTH TO MAXIMIZE THE LIVETIME OF THE NEXO DETECTOR" (2025). Theses and Dissertations--Physics and Astronomy. 134.
https://uknowledge.uky.edu/physastron_etds/134
