Date of Award
Open Access Dissertation
Physics and Astronomy
Frank T. Avignone
A framework to search for a triple-proton decay of 130Te in the CUORE detector against a background of muons is presented. We use machine learning to classify different kinds of energy depositing events. We use the classification information to improve our detection or non-detection limits of a triple-proton decay process. We derive and use a methodology of combining Poisson counting statistics with supervised classification machine learning tools. Additionally, a sensitivity calculation is provided which uses the classification counting likelihood. Using our analysis technique, we achieve an lower 2σ half-life bound of 7.43×1024yrs for triple-proton decay of 130Te.
Adams, D.(2022). Search for Triple-Proton Decay Using Machine Learning With CUORE. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/7115