NEXT Collaboration(Ferrario, P. et al), Benlloch-Rodriguez, J. M., Kekic, M., Renner, J., Uson, A., Alvarez, V., et al. (2019). Demonstration of the event identification capabilities of the NEXT-White detector. J. High Energy Phys., 10(10), 052–20pp.
Abstract: In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a Th-228 calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of 71.6 +/- 1.5(stat) +/- 0.3(sys) % for a background acceptance of 20.6 +/- 0.4(stat) +/- 0.3(sys)% is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies.
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NEXT Collaboration(Henriques, C. A. O. et al), Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., Carrion, J. V., et al. (2019). Electroluminescence TPCs at the thermal diffusion limit. J. High Energy Phys., 01(1), 027–23pp.
Abstract: The NEXT experiment aims at searching for the hypothetical neutrinoless double-beta decay from the Xe-136 isotope using a high-purity xenon TPC. Efficient discrimination of the events through pattern recognition of the topology of primary ionisation tracks is a major requirement for the experiment. However, it is limited by the diffusion of electrons. It is known that the addition of a small fraction of a molecular gas to xenon reduces electron diffusion. On the other hand, the electroluminescence (EL) yield drops and the achievable energy resolution may be compromised. We have studied the effect of adding several molecular gases to xenon (CO2, CH4 and CF4) on the EL yield and energy resolution obtained in a small prototype of driftless gas proportional scintillation counter. We have compared our results on the scintillation characteristics (EL yield and energy resolution) with a microscopic simulation, obtaining the diffusion coefficients in those conditions as well. Accordingly, electron diffusion may be reduced from about 10 for pure xenon down to 2.5 using additive concentrations of about 0.05%, 0.2% and 0.02% for CO2, CH4 and CF4, respectively. Our results show that CF4 admixtures present the highest EL yield in those conditions, but very poor energy resolution as a result of huge fluctuations observed in the EL formation. CH4 presents the best energy resolution despite the EL yield being the lowest. The results obtained with xenon admixtures are extrapolated to the operational conditions of the NEXT-100 TPC. CO2 and CH4 show potential as molecular additives in a large xenon TPC. While CO2 has some operational constraints, making it difficult to be used in a large TPC, CH4 shows the best performance and stability as molecular additive to be used in the NEXT-100 TPC, with an extrapolated energy resolution of 0.4% at 2.45 MeV for concentrations below 0.4%, which is only slightly worse than the one obtained for pure xenon. We demonstrate the possibility to have an electroluminescence TPC operating very close to the thermal diffusion limit without jeopardizing the TPC performance, if CO2 or CH4 are chosen as additives.
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NEXT Collaboration(Kekic, M. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys., 01(1), 189–22pp.
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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NEXT Collaboration(Renner, J. et al), Benlloch-Rodriguez, J., Botas, A., Ferrario, P., Gomez-Cadenas, J. J., Alvarez, V., et al. (2017). Background rejection in NEXT using deep neural networks. J. Instrum., 12, T01004–21pp.
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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NEXT Collaboration(Henriques, C. A. O. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J., Botas, A., Carcel, S., et al. (2017). Secondary scintillation yield of xenon with sub-percent levels of CO2 additive for rare-event detection. Phys. Lett. B, 773, 663–671.
Abstract: Xe-CO2 mixtures are important alternatives to pure xenon in Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification with applications in the important field of rare event detection such as directional dark matter, double electron capture and double beta decay detection. The addition of CO2 to pure xenon at the level of 0.05-0.1% can reduce significantly the scale of electron diffusion from 10 mm/root m to 2.5 mm/root m, with high impact on the discrimination of the events through pattern recognition of the topology of primary ionization trails. We have measured the electroluminescence (EL) yield of Xe-CO2 mixtures, with sub-percent CO2 concentrations. We demonstrate that the EL production is still high in these mixtures, 70% and 35% relative to that produced in pure xenon, for CO2 concentrations around 0.05% and 0.1%, respectively. The contribution of the statistical fluctuations in EL production to the energy resolution increases with increasing CO2 concentration, being smaller than the contribution of the Fano factor for concentrations below 0.1% CO2.
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