Xenon Signal Denoising via Supervised, Semi-Supervised, and Unsupervised Models

This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model achieves an energy resolution of $<1\mathrm{\%}$, while the semi-supervised models achieve energy resolutions of $\sim 1\mathrm{\%}$, and the unsupervised model performance is $\sim 1.5\mathrm{\%}$. This work is evidence that machine learning denoising can improve energy resolution compared to traditional algorithms, even when experimentalists lack perfect a priori knowledge of the signals. Such models provide a realistic path toward next-generation sensitivity in $0\nu \beta \beta$ searches.