Douglas Adams

Date of Award

Fall 2022

Document Type

Open Access Dissertation


Physics and Astronomy

First Advisor

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.


© 2022, Douglas Adams

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