Conference on Science at the Sanford Underground Research Facility

Enhancing XENONnT's Sensitivity to Neutrinoless Double-beta Decay with TextCNN

May 14, 2024, 3:00 PM

20m

CB 205 (SD Mines)

Oral Double Beta Decay Double Beta Decay

Speaker

Min Zhong (University of California, San Diego)

Description

XENONnT employs a large target mass and dual-phase TPC to achieve unparalleled sensitivity in rare event searches. The neutrinoless double-beta ($0\nu \beta \beta$) decay searches at XENONnT encounters 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 $0\nu \beta \beta$ searches at XENONnT, which can potentially improve the sensitivity of the $0\nu \beta \beta$ search for ${}^{136}$Xe by over 30%. This highlights the potential for utilizing ${}^{136}$Xe enriched xenon to achieve heightened sensitivity to $0\nu \beta \beta$ decay in future dark matter experiments such as XLZD.

Presentation materials

CoSSURF2024_MinZhong_v4.pdf