# Simulation of charge readout with segmented tiles in nEXO

## TL;DR

This paper simulates the charge readout performance of segmented tiles in the nEXO detector, demonstrating improved background discrimination and sensitivity for detecting neutrinoless double beta decay using advanced analysis methods.

## Contribution

It introduces a detailed simulation of charge collection with segmented tiles and applies machine learning techniques to enhance signal-background discrimination in nEXO.

## Key findings

- Charge readout with segmented tiles improves background rejection.
- Deep neural networks increase sensitivity by up to 32%.
- Simulation indicates promising detection capabilities for nEXO.

## Abstract

nEXO is a proposed experiment to search for the neutrino-less double beta decay ($0\nu\beta\beta$) of $^{136}$Xe in a tonne-scale liquid xenon time projection chamber (TPC). The nEXO TPC will be equipped with charge collection tiles to form the anode. In this work, the charge reconstruction performance of this anode design is studied with a dedicated simulation package. A multi-variate method and a deep neural network are developed to distinguish simulated $0\nu\beta\beta$ signals from backgrounds arising from trace levels of natural radioactivity in the detector materials. These simulations indicate that the nEXO TPC with charge-collection tiles shows promising capability to discriminate the $0\nu\beta\beta$ signal from backgrounds. The estimated half-life sensitivity for $0\nu\beta\beta$ decay is improved by $\sim$20$~(32)\%$ with the multi-variate~(deep neural network) methods considered here, relative to the sensitivity estimated in the nEXO pre-conceptual design report.