NEXT Collaboration(Martinez-Lema, G. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Sensitivity of the NEXT experiment to Xe-124 double electron capture. J. High Energy Phys., 02(2), 203–25pp.
Abstract: Double electron capture by proton-rich nuclei is a second-order nuclear process analogous to double beta decay. Despite their similarities, the decay signature is quite different, potentially providing a new channel to measure the hypothesized neutrinoless mode of these decays. The Standard-Model-allowed two-neutrino double electron capture (2 nu EC EC) has been predicted for a number of isotopes, but only observed in Kr-78, Ba-130 and, recently, Xe-124. The sensitivity to this decay establishes a benchmark for the ultimate experimental goal, namely the potential to discover also the lepton-number-violating neutrinoless version of this process, 0 nu EC EC. Here we report on the current sensitivity of the NEXT-White detector to Xe-124 2 nu EC EC and on the extrapolation to NEXT-100. Using simulated data for the 2 nu EC EC signal and real data from NEXT-White operated with Xe-124-depleted gas as background, we define an optimal event selection that maximizes the NEXT-White sensitivity. We estimate that, for NEXT-100 operated with xenon gas isotopically enriched with 1 kg of Xe-124 and for a 5-year run, a sensitivity to the 2 nu EC EC half-life of 6 x 10(22) y (at 90% confidence level) or better can be reached.
|
NEXT Collaboration(Ferrario, P. et al), Laing, A., Lopez-March, N., Gomez-Cadenas, J. J., Alvarez, V., Carcel, S., et al. (2016). First proof of topological signature in the high pressure xenon gas TPC with electroluminescence amplification for the NEXT experiment. J. High Energy Phys., 01(1), 104–18pp.
Abstract: The NEXT experiment aims to observe the neutrinoless double beta decay of Xe-136 in a high-pressure xenon gas TPC using electroluminescence (EL) to amplify the signal from ionization. One of the main advantages of this technology is the possibility to reconstruct the topology of events with energies close to Q(beta beta). This paper presents the first demonstration that the topology provides extra handles to reject background events using data obtained with the NEXT-DEMO prototype. Single electrons resulting from the interactions of Na-22 1275 keV gammas and electron-positron pairs produced by conversions of gammas from the Th-228 decay chain were used to represent the background and the signal in a double beta decay. These data were used to develop algorithms for the reconstruction of tracks and the identification of the energy deposited at the end-points, providing an extra background rejection factor of 24.3 +/- 1.4 (stat.)%, while maintaining an efficiency of 66.7 +/- 1.% for signal events.
|
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.
|
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.
|
