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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. |
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Title  |
Application and performance of an ML-EM algorithm in NEXT |
Type |
Journal Article |
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Year |
2017 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
12 |
Issue |
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Pages |
P08009 - 22pp |
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Keywords |
Gaseous imaging and tracking detectors; Image reconstruction in medical imaging; Time projection Chambers (TPC); Medical-image reconstruction methods and algorithms; computer-aided software |
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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. |
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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 |
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Publisher |
Iop Publishing Ltd |
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English |
Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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ISSN |
1748-0221 |
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Expedition |
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Conference |
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Notes |
WOS:000414159500009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3358 |
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Author |
NEXT Collaboration (Renner, J. et al); Benlloch-Rodriguez, J.; Botas, A.; Ferrario, P.; Gomez-Cadenas, J.J.; Alvarez, V.; Carcel, S.; Carrion, J.V.; Cervera-Villanueva, A.; Diaz, J.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Lorca, D.; Martinez, A.; Monrabal, F.; Muñoz Vidal, J.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Querol, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N. |
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Title |
Background rejection in NEXT using deep neural networks |
Type |
Journal Article |
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Year |
2017 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
12 |
Issue |
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Pages |
T01004 - 21pp |
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Keywords |
Analysis and statistical methods; Pattern recognition; cluster finding; calibration and fitting methods; Double-beta decay detectors; Time projection chambers |
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Abstract |
We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement. |
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Address |
[Renner, J.; Munoz Vidal, J.; Benlloch-Rodriguez, J. M.; Botas, A.; Ferrario, P.; Gomez-Cadenas, J. J.; Alvarez, V.; Carcel, S.; Carrion, J. V.; Cervera, A.; Diaz, J.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Lorca, D.; Martinez, A.; Monrabal, F.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Querol, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N.] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Valencia 46980, Spain, Email: jrenner@ific.uv.es |
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Publisher |
Iop Publishing Ltd |
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English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000395770200004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2995 |
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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. |
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Title |
Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution |
Type |
Journal Article |
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Year |
2021 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
07 |
Issue |
7 |
Pages |
146 - 38pp |
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Keywords |
Dark Matter and Double Beta Decay (experiments) |
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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. |
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Address |
[Hauptman, J.] Iowa State Univ, Dept Phys & Astron, Ames, IA USA, Email: ander@post.bgu.ac.il; |
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Publisher |
Springer |
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English |
Summary Language |
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Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1029-8479 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:000677621700001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4906 |
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| Permanent link to this record |
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Author |
NEXT Collaboration (Martinez-Lema, G. et al); Palmeiro, B.; Botas, A.; Laing, A.; Renner, J.; Simon, A.; Alvarez, V.; Benlloch-Rodriguez, J.M.; Carcel, S.; Carrion, J.V.; Diaz, J.; Felkai, R.; Kekic, M.; Lopez-March, N.; Martinez, A.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Perez, J.; Querol, M.; Rodriguez, J.; Romo-Lugue, C.; Sorel, M.; Torrent, J.; Yahlali, N. |
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Title |
Calibration of the NEXT-White detector using Kr-83m decays |
Type |
Journal Article |
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Year |
2018 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
13 |
Issue |
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Pages |
P10014 - 21pp |
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Keywords |
Charge transport; multiplication and electroluminescence in rare gases and liquids; Gaseous imaging and tracking detectors; Time projection Chambers (TPC); Double-beta decay detectors |
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Abstract |
The NEXT-White (NEW) detector is currently the largest radio-pure high-pressure xenon gas time projection chamber with electroluminescent readout in the world. It has been operating at Laboratorio Subterraneo de Canfranc (LSC) since October 2016. This paper describes the calibrations performed using Kr-83m decays during a long run taken from March to November 2017 (Run II). Krypton calibrations are used to correct for the finite drift-electron lifetime as well as for the dependence of the measured energy on the event transverse position which is caused by variations in solid angle coverage both for direct and reflected light and edge effects. After producing calibration maps to correct for both effects we measure an excellent energy resolution for 41.5 keV point-like deposits of (4.553 +/- 0.010 (stat.) +/- 0.324 (sys.)) % FWHM in the full chamber and (3.804 +/- 0.013 (stat.) +/- 0.112 (sys.)) % FWHM in a restricted fiducial volume. Using naive 1/root E scaling, these values translate into resolutions of (0.5916 +/- 0.0014 (stat.) +/- 0.0421 (sys.)) % FWHM and (0.4943 +/- 0.0017 (stat.) +/- 0.0146 (sys.)) % FWHM at the Q(beta beta) energy of xenon double beta decay (2458 keV), well within range of our target value of 1%. |
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Address |
[Hauptman, J.] Iowa State Univ, Dept Phys & Astron, 12 Phys Hall, Ames, IA 50011 USA, Email: gonzalo.martinez.lema@usc.es |
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Publisher |
Iop Publishing Ltd |
Place of Publication |
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English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000447061800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3754 |
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Author |
NEXT Collaboration (Kekic, M. et al); Benlloch-Rodriguez, J.M.; Carcel, S.; Carrion, J.V.; Diaz, J.; Felkai, R.; Lopez-March, N.; Martin-Albo, J.; Martinez, A.; Martinez-Lema, G.; Martinez-Vara, M.; Muñoz Vidal, J.; Novella, P.; Palmeiro, B.; Querol, M.; Renner, J.; Romo-Luque, C.; Sorel, M.; Uson, A.; Yahlali, N. |
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Title |
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment |
Type |
Journal Article |
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Year |
2021 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
01 |
Issue |
1 |
Pages |
189 - 22pp |
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Keywords |
Dark Matter and Double Beta Decay (experiments) |
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Abstract |
Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. |
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Address |
[Hauptman, J.; Nygren, D. R.] Iowa State Univ, Dept Phys & Astron, 12 Phys Hall, Ames, IA 50011 USA, Email: marija.kekic@usc.es |
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Corporate Author |
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Thesis |
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Publisher |
Springer |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1029-8479 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000616730800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4729 |
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