Application and performance of an ML-EM algorithm in NEXT

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.

19 pages, 16 figures

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• Demonstration of the event identification capabilities of the NEXT-White detector
• Measurement of radon-induced backgrounds in the NEXT double beta decay experiment
• Double Beta Decay Experiments at Canfranc Underground Laboratory
• A journey to ITACA: Ion Tracking with Ammonium Cations Apparatus