Enhancing Neutrinoless Double-Beta Decay Sensitivity of Liquid-Xenon Time Projection Chamber with Augmented Convolutional Neural Network

TL;DR

This paper introduces an augmented convolutional neural network that significantly improves background rejection in liquid xenon TPC detectors, enhancing their sensitivity for neutrinoless double-beta decay searches.

Contribution

The study develops and applies an augmented CNN model to LXe TPC data, boosting background rejection and sensitivity for $0 uetaeta$ detection beyond previous methods.

Findings

Achieved over 60% background rejection with 90% signal acceptance.

Projected a 40% improvement in $^{136}$Xe $0 uetaeta$ search sensitivity.

Validated model performance using simulation and calibration data from XENONnT.

Abstract

Dual-phase time projection chamber (TPC) that employs a multi-ton-scale liquid xenon (LXe) target mass is a pioneering detector technology to search for dark matter. Beyond its advantage in dark matter direct detection efforts, the natural xenon target allows it to search for the neutrinoless double-beta decay ($0\nu\beta\beta$) process, which would violate lepton number conservation and indicate that neutrinos are Majorana particles. However, such $0\nu\beta\beta$ searches have been limited by gamma-ray backgrounds originating from the detector materials. In this work, we designed an augmented convolutional neural network (A-CNN) model to extract additional event-topology information from detector data. Using simulation and calibration data from XENONnT, a leading LXe TPC experiment, our model achieved over 60% background rejection while maintaining 90% signal acceptance. This rejection power improves XENONnT's projected sensitivity of the $^{136}$Xe $0\nu\beta\beta$ search by about 40%. The implementation of A-CNN in the data analysis of future liquid xenon observatories, such as XLZD, will further enhance their sensitivities for $0\nu\beta\beta$ with $^{136}$Xe.