Application and performance of an ML-EM algorithm in NEXT
dc.contributor.author
dc.date.accessioned
2019-09-17T11:24:35Z
dc.date.available
2019-09-17T11:24:35Z
dc.date.issued
2017-08-16
dc.identifier.issn
1748-0221
dc.identifier.uri
dc.description.abstract
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
dc.description.sponsorship
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
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Physics (IOP)
dc.relation.isformatof
Versió preprint del document publicat a: https://doi.org/10.1088/1748-0221/12/08/P08009
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© Journal of Instrumentation, 2017, vol. 12, art. P08009
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Articles publicats (D-EMCI)
dc.rights
Tots els drets reservats
dc.title
Application and performance of an ML-EM algorithm in NEXT
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/submittedVersion
dc.identifier.doi
dc.identifier.idgrec
027553