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NEXT Collaboration (Alvarez, V. et al); Carcel, S.; Cervera-Villanueva, A.; Diaz, J.; Ferrario, P.; Gil, A.; Gomez-Cadenas, J.J.; Laing, A.; Liubarsky, I.; Lorca, D.; Martin-Albo, J.; Martinez, A.; Monrabal, F.; Muñoz Vidal, J.; Nebot-Guinot, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N. |
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Title |
Initial results of NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment |
Type |
Journal Article |
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Year |
2013 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
8 |
Issue |
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Pages |
P04002 - 25pp |
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Keywords |
Double-beta decay detectors; Time projection chambers; Pattern recognition, cluster finding, calibration and fitting methods |
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Abstract |
NEXT-DEMO is a large-scale prototype of the NEXT-100 detector, an electroluminescent time projection chamber that will search for the neutrinoless double beta decay of Xe-136 using 100-150 kg of enriched xenon gas. NEXT-DEMO was built to prove the expected performance of NEXT-100, namely, energy resolution better than 1% FWHM at 2.5MeV and event topological reconstruction. In this paper we describe the prototype and its initial results. A resolution of 1.75% FWHM at 511 keV (which extrapolates to 0.8% FWHM at 2.5 MeV) was obtained at 10 bar pressure using a gamma-ray calibration source. Also, a basic study of the event topology along the longitudinal coordinate is presented, proving that it is possible to identify the distinct dE/dx of electron tracks in high-pressure xenon using an electroluminescence TPC. |
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CSIC, Inst Fis Corpuscular IFIC, Valencia 46980, Spain, Email: justo.martin-albo@ific.uv.es |
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Iop Publishing Ltd |
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English |
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1748-0221 |
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Notes |
WOS:000317462400009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1414 |
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Author |
NEXT Collaboration (Alvarez, V. et al); Carcel, S.; Cervera-Villanueva, A.; Diaz, J.; Ferrario, P.; Gil, A.; Gomez-Cadenas, J.J.; Laing, A.; Liubarsky, I.; Lorca, D.; Martin-Albo, J.; Martinez, A.; Monrabal, F.; Muñoz Vidal, J.; Nebot-Guinot, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N. |
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Title |
Operation and first results of the NEXT-DEMO prototype using a silicon photomultiplier tracking array |
Type |
Journal Article |
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Year |
2013 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
8 |
Issue |
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Pages |
P09011 - 20pp |
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Keywords |
Pattern recognition, cluster finding, calibration and fitting methods; Double-beta decay detectors; Particle tracking detectors (Gaseous detectors); Time projection chambers |
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Abstract |
NEXT-DEMO is a high-pressure xenon gas TPC which acts as a technological test-bed and demonstrator for the NEXT-100 neutrinoless double beta decay experiment. In its current configuration the apparatus fully implements the NEXT-100 design concept. This is an asymmetric TPC, with an energy plane made of photomultipliers and a tracking plane made of silicon photomultipliers (SiPM) coated with TPB. The detector in this new configuration has been used to reconstruct the characteristic signature of electrons in dense gas, demonstrating the ability to identify the MIP and “blob” regions. Moreover, the SiPM tracking plane allows for the definition of a large fiducial region in which an excellent energy resolution of 1.82% FWHM at 511 keV has been measured (a value which extrapolates to 0.83% at the xenon Q(beta beta)). |
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Address |
[Alvarez, V.; Carcel, S.; Cervera, A.; Diaz, J.; Ferrario, P.; Gil, A.; Gomez-Cadenas, J. J.; Laing, A.; Liubarsky, I.; Lorca, D.; Martin-Albo, J.; Martinez, A.; Monrabal, F.; Monserrate, M.; Munoz Vidal, J.; Nebot-Guinot, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N.] CSIC, Inst Fis Corpuscular IFIC, Valencia 46980, Spain, Email: andrew.laing@ific.uv.es; |
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Publisher |
Iop Publishing Ltd |
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English |
Summary Language |
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Abbreviated Series Title |
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Series Issue |
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ISSN |
1748-0221 |
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Conference |
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Notes |
WOS:000326680200025 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1653 |
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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 |
Ortiz Arciniega, J.L.; Carrio, F.; Valero, A. |
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Title |
FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions |
Type |
Journal Article |
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Year |
2019 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
14 |
Issue |
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Pages |
P09002 - 13pp |
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Keywords |
Data processing methods; Pattern recognition; cluster finding; calibration and fitting methods; Simulation methods and programs |
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Abstract |
The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented. |
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Address |
[Ortiz Arciniega, J. L.] Univ Valencia, Avinguda Univ S-N, Burjassot, Spain, Email: orarjo@alumni.uv.es |
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Corporate Author |
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Thesis |
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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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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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Conference |
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Notes |
WOS:000486990000002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
4150 |
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Author |
Renner, J.; Cervera-Villanueva, A.; Hernando, J.A.; Izmaylov, A.; Monrabal, F.; Muñoz, J.; Nygren, D.; Gomez-Cadenas, J.J. |
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Title |
Improved background rejection in neutrinoless double beta decay experiments using a magnetic field in a high pressure xenon TPC |
Type |
Journal Article |
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Year |
2015 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
10 |
Issue |
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Pages |
P12020 - 19pp |
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Keywords |
Pattern recognition, cluster finding, calibration and fitting methods; Double-beta decay detectors; Time projection chambers; Particle tracking detectors (Gaseous detectors) |
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Abstract |
We demonstrate that the application of an external magnetic field could lead to an improved background rejection in neutrinoless double-beta (0 nu beta beta) decay experiments using a high-pressure xenon (HPXe) TPC. HPXe chambers are capable of imaging electron tracks, a feature that enhances the separation between signal events (the two electrons emitted in the 0 nu beta beta decay of Xe-136) and background events, arising chiefly from single electrons of kinetic energy compatible with the end-point of the 0 nu beta beta decay (Q(beta beta)). Applying an external magnetic field of sufficiently high intensity (in the range of 0.5-1 Tesla for operating pressures in the range of 5-15 atmospheres) causes the electrons to produce helical tracks. Assuming the tracks can be properly reconstructed, the sign of the curvature can be determined at several points along these tracks, and such information can be used to separate signal (0 nu beta beta) events containing two electrons producing a track with two different directions of curvature from background (single-electron) events producing a track that should spiral in a single direction. Due to electron multiple scattering, this strategy is not perfectly efficient on an event-by-event basis, but a statistical estimator can be constructed which can be used to reject background events by one order of magnitude at a moderate cost (about 30%) in signal efficiency. Combining this estimator with the excellent energy resolution and topological signature identification characteristic of the HPXe TPC, it is possible to reach a background rate of less than one count per ton-year of exposure. Such a low background rate is an essential feature of the next generation of 0 nu beta beta experiments, aiming to fully explore the inverse hierarchy of neutrino masses. |
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Address |
[Renner, J.; Imzaylov, A.; Monrabal, F.; Munoz, J.; Gomez-Cadenas, J. J.] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Valencia 46980, Spain, Email: jrenner@ific.uv.es |
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Corporate Author |
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Thesis |
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Publisher |
Iop Publishing Ltd |
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 |
1748-0221 |
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:000369998500053 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
2549 |
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| Permanent link to this record |
