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 $$<1%$$ < 1 % , while the semi-supervised models achieve energy resolutions of ~ 1% ∼ 1 % , and the unsupervised model performance is ~ 1.5% ∼ 1.5 % . 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 0ν β β 0 ν β β searches.