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Author NEXT Collaboration (Simon, A. et al); Carcel, S.; Carrion, J.V.; Diaz, J.; Felkai, R.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Martinez-Vara, M.; Muñoz Vidal, J.; Novella, P.; Palmeiro, B.; Querol, M.; Renner, J.; Romo-Luque, C.; Sorel, M.; Uson, A.; Yahlali, N.
Title (down) Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution Type Journal Article
Year 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 07 Issue 7 Pages 146 - 38pp
Keywords Dark Matter and Double Beta Decay (experiments)
Abstract Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of similar to 10(27) yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of <similar to> 5 when reconstructing electron-positron pairs in the Tl-208 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterraneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of similar to 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e(-)e(+) pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.
Address [Hauptman, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA, Email: ander@post.bgu.ac.il;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1029-8479 ISBN Medium
Area Expedition Conference
Notes WOS:000677621700001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4906
Permanent link to this record
 
 
Author NEXT Collaboration (Simon, A. et al); Gomez-Cadenas, J. J.; Alvarez, V.; Benlloch-Rodriguez, J. M.; Botas, A.; Carcel, S.; Carrion, J.V.; Diaz, J.; Felkai, R.; Ferrario, P.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Perez, J.; Querol, M.; Renner, J.; Rodriguez, J.; Sorel, M.; Torrent, J.; Yahlali, N.
Title (down) Application and performance of an ML-EM algorithm in NEXT Type Journal Article
Year 2017 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 12 Issue Pages P08009 - 22pp
Keywords Gaseous imaging and tracking detectors; Image reconstruction in medical imaging; Time projection Chambers (TPC); Medical-image reconstruction methods and algorithms; computer-aided software
Abstract 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.
Address [Simon, A.; Gomez-Cadenas, J. J.; Alvarez, V.; Benlloch-Rodriguez, J. M.; Botas, A.; Carcel, S.; Carrion, J. V.; Diaz, J.; Felkai, R.; Ferrario, P.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Munoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Perez, J.; Querol, M.; Renner, J.; Rodriguez, J.; Sorel, M.; Torrent, J.; Yahlali, N.] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Valencia 46980, Spain, Email: ander.simon@ific.uv.es
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000414159500009 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 3358
Permanent link to this record