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Simón Estévez, Ander
Lerche, C. Monrabal Capilla, Francesc Gómez Cadenas, Juan Álvarez Puerta, Vicente Azevedo, C.D.R. Benlloch Rodríguez, J.M. Borges, Filipa I.G.M. Botas, A. Cárcel García, Sara Carrión, J.V. Cebrián, Susana Conde, Carlos A.N. Díaz Medina, José Diesburg, M. Escada, J. Esteve, Raúl Felkai, R. Fernandes, L.M.P. Ferrario, Paola Ferreira, Antonio Luis Freitas, Elisabete D.C. Goldschmidt, Azriel González-Díaz, Diego Gutiérrez, Rafael María Hauptman, John M. Henriques, C.A.O. Hernández, Andrés I. Hernando Morata, J.A. Herrero, Vicente Jones, Benjamin J.P. Labarga, Luis A. Laing, Andrew Lebrun, P. Liubarsky, Igor López-March, N. Losada, Marta Martín-Albo Simón, Justo Martínez Lema, Gonzalo Martínez Pérez, Alberto McDonald, Alison D. Monteiro, Cristina M.B. Mora, Francisco José Moutinho, L.M. Muñoz Vidal, J. Musti, M. Nebot Guinot, Miquel Novella, P. Nygren, David R. Palmeiro, B. Para, A. Pérez, Javier Martin Querol, M. Renner, Joshua Ripoll Masferrer, Lluís Rodríguez Samaniego, Javier Rogers, L. Santos, Filomena P. dos Santos, Joaquim M.F. Sofka, C. Sorel, Michel Stiegler, T. Toledo, J.F. Torrent Collell, Jordi Tsamalaidze, Zviadi Veloso, João F.C.A. Webb, R.C. White, James T. Yahlali Haddou, Nadia |
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| 2020 February 15 | |
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The goal of the NEXT experiment is the observation of neutrinoless double beta decay in 136Xe 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 The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the Ministerio de Economía y Competitividad of Spain under grants FIS2014-53371-C04 and the Severo Ochoa Program SEV-2014-0398; the GVA of Spain under grant PROMETEO/2016/120; the Portuguese FCT and FEDER through the program COMPETE, project PTDC/FIS/103860/2008; the U.S. Department of Energy under contracts number DE-AC02-07CH11359 (Fermi National Accelerator Laboratory) and DE-FG02-13ER42020 (Texas A&M); and the University of Texas at Arlington |
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| http://hdl.handle.net/2072/372194 | |
| eng | |
| Institute of Physics (IOP) | |
| Tots els drets reservats | |
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Detectors òptics
Optical detectors Detectors de radiació Nuclear counters |
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| Application and performance of an ML-EM algorithm in NEXT | |
| info:eu-repo/semantics/article | |
| Recercat |
