# Application and performance of an ML-EM algorithm in NEXT

## TL;DR

This paper presents the application of a bi-dimensional ML-EM algorithm for event reconstruction in the NEXT experiment, achieving near-optimal energy resolution in simulated data for neutrinoless double beta decay detection.

## Contribution

It introduces a bi-dimensional ML-EM reconstruction method tailored for the NEXT detector, demonstrating its effectiveness in simulation for energy resolution.

## Key findings

- Achieves better than 0.5% FWHM energy resolution in simulations
- Reconstructs transverse event projections using integrated photosensor signals
- Demonstrates potential for improved event reconstruction in neutrinoless double beta decay searches

## Abstract

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in $^{136}$Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of $^{136}$Xe) for events distributed over the full active volume of the TPC.