Application and performance of an ML-EM algorithm in NEXT

作者：Simon A^*; Lerche C; Monrabal F; Gomez Cadenas J J; Alvarez V; Azevedo C D R; Benlloch Rodriguez J M; Borges F I G M; Botas A; Carcel S; Carrion J V; Cebrian S; Conde C A N; Diaz J; Die**urg M; Escada J; Esteve R; Felkai R; Fernandes L M P; Ferrario P; Ferreira A L; Freitas E D C; Goldschmidt A; Gonzalez Diaz D; Gutierrez R M; Hauptman J; Henriques C A O; Hernandez A I; Hernando Morata J A; Herrero V; Jones B J P

来源：Journal of Instrumentation, 2017, 12(08): P08009.

DOI：10.1088/1748-0221/12/08/P08009

• Gaseous imaging and tracking detectors
• Image reconstruction in medical imaging
• Time projection Chambers (TPC)
• Medical-image reconstruction methods and algorithms
• computer-aided software

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in Xe-136 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 136Xe) for events distributed over the full active volume of the TPC.

• 出版日期2017-8