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

Abstract

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 $<1\%$, while the semi-supervised models achieve energy resolutions of $\sim 1\%$, and the unsupervised model performance is $\sim 1.5\%$. 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 $0\nu \beta \beta$ searches.

10 pages, 5 figures, updated to correct typos and formatting

References in corpus (11)

• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Neutrinoless Double-Beta Decay: Status and Prospects
• Sensitivity and discovery potential of the proposed nEXO experiment to neutrinoless double beta decay
• NEXO: Neutrinoless double beta decay search beyond $10^{28}$ year half-life sensitivity
• Neutrinoless Double Beta Decay and the Baryon Asymmetry of the Universe
• Performance of novel VUV-sensitive Silicon Photo-Multipliers for nEXO
• Simulation of charge readout with segmented tiles in nEXO
• Robust multi-coil MRI reconstruction via self-supervised denoising
• A new method for structural diagnostics with muon tomography and deep learning
• Performance of a convolutional autoencoder designed to remove electronic noise from p-type point contact germanium detector signals
• Neural Network Reconstruction of Non-Gaussian Initial Conditions from Dark Matter Halos