Enhancing XENONnT's Sensitivity to Neutrinoless Double Beta Decay with TextCNN

XENONnT employs a large target mass and dual-phase TPC to achieve unparalleled sensitivity in rare event searches. The neutrinoless double-beta (0𝜈𝛽𝛽) decay searches at XENONnT encounter limitations due to gamma-rays emitted by the detector material. Therefore, a TextCNN (convolutional neural network for text) model with waveform augmentation is designed to extract maximum information from the detector data. It demonstrates remarkable capability, achieving over 60% background rejection while maintaining a 90% signal acceptance. It significantly improved the background rejection for 0𝜈𝛽𝛽 searches at XENONnT, which can potentially improve the sensitivity of the 0𝜈𝛽𝛽 search for ¹³⁶Xe by over 30%. This highlights the potential of future dark matter experiments such as XLZD to achieve heightened sensitivity to 0𝜈𝛽𝛽 decay .