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arXiv · 1705.10270

Application and performance of an ML-EM algorithm in NEXT

NEXT Collaboration·A. Simón·C. Lerche·F. Monrabal·J. J. Gómez-Cadenas·V. Álvarez·C. D. R. Azevedo·J. M. Benlloch-Rodríguez·F. I. G. M. Borges·A. Botas·S. Cárcel·J. V. Carrión·S. Cebrián·C. A. N. Conde·J. Díaz·M. Diesburg·J. Escada·R. Esteve·R. Felkai·L. M. P. Fernandes·P. Ferrario·A. L. Ferreira·E. D. C. Freitas·A. Goldschmidt·D. González-Díaz·R. M. Gutiérrez·J. Hauptman·C. A. O. Henriques·A. I. Hernandez·J. A. Hernando Morata·V. Herrero·B. J. P. Jones·L. Labarga·A. Laing·P. Lebrun·I. Liubarsky·N. López-March·M. Losada·J. Martín-Albo·G. Martínez-Lema·A. Martínez·A. D. McDonald·C. M. B. Monteiro·F. J. Mora·L. M. Moutinho·J. Muñoz Vidal·M. Musti·M. Nebot-Guinot·P. Novella·D. R. Nygren·B. Palmeiro·A. Para·J. Pérez·M. Querol·J. Renner·L. Ripoll·J. Rodríguez·L. Rogers·F. P. Santos·J. M. F. dos Santos·C. Sofka·M. Sorel·T. Stiegler·J. F. Toledo·J. Torrent·Z. Tsamalaidze·J. F. C. A. Veloso·R. Webb·J. T. White·N. Yahlali
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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.

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NEXT Collaboration, A. Simón, C. Lerche, F. Monrabal, J. J. Gómez-Cadenas, V. Álvarez, C. D. R. Azevedo, J. M. Benlloch-Rodríguez, F. I. G. M. Borges, A. Botas, S. Cárcel, J. V. Carrión, S. Cebrián, C. A. N. Conde, J. Díaz, M. Diesburg, J. Escada, R. Esteve, R. Felkai, L. M. P. Fernandes, P. Ferrario, A. L. Ferreira, E. D. C. Freitas, A. Goldschmidt, D. González-Díaz, R. M. Gutiérrez, J. Hauptman, C. A. O. Henriques, A. I. Hernandez, J. A. Hernando Morata, V. Herrero, B. J. P. Jones, L. Labarga, A. Laing, P. Lebrun, I. Liubarsky, N. López-March, M. Losada, J. Martín-Albo, G. Martínez-Lema, A. Martínez, A. D. McDonald, C. M. B. Monteiro, F. J. Mora, L. M. Moutinho, J. Muñoz Vidal, M. Musti, M. Nebot-Guinot, P. Novella, D. R. Nygren, B. Palmeiro, A. Para, J. Pérez, M. Querol, J. Renner, L. Ripoll, J. Rodríguez, L. Rogers, F. P. Santos, J. M. F. dos Santos, C. Sofka, M. Sorel, T. Stiegler, J. F. Toledo, J. Torrent, Z. Tsamalaidze, J. F. C. A. Veloso, R. Webb, J. T. White, N. Yahlali. 2017-05-29. Application and performance of an ML-EM algorithm in NEXT. https://doi.org/10.1088/1748-0221%2F12%2F08%2Fp08009

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