Search for the ${}^{16}\text{O}(ppp){\to }^{13}\text{C}{\pi }^{+}{\pi }^{+}{e}^{+}$ Decay Mode in Super-Kamiokande Using Machine Learning Techniques

We report a new partial lifetime limit of $4.2\times {10}^{32}$ years for the trinucleon decay mode ${}^{16}\text{O}(ppp){\to }^{13}\text{C}{\pi }^{+}{\pi }^{+}{e}^{+}$, obtained from a search conducted using the Super-Kamiokande detector with 0.401 megaton-years of exposure across five operational periods (SK-I: 1996–2001, SK-II: 2002–2005, SK-III: 2006–2008, SK-IV: 2008–2018, SK-V: 2019–2020). This represents an improvement of six orders of magnitude over previous experimental constraints. The analysis utilizes a convolutional neural network (CNN) incorporating an attention mechanism—a computational technique that enables the model to focus on the most relevant regions of Cherenkov ring patterns—to enhance event classification, thereby improving the sensitivity of the search. This is the first application of a CNN to a nucleon decay search in Super-Kamiokande. Furthermore, the large dataset available in Super-Kamiokande (hereafter “SK”) strengthens the statistical power of the study, enabling a more stringent constraint than those set by prior experiments.