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NEXT Collaboration(Novella, P. et al), Palmeiro, B., Simon, A., Sorel, M., Martinez-Lema, G., Alvarez, V., et al. (2018). Measurement of radon-induced backgrounds in the NEXT double beta decay experiment. J. High Energy Phys., 10(10), 112–27pp.
Abstract: The measurement of the internal Rn-222 activity in the NEXT-White detector during the so-called Run-II period with Xe-136-depleted xenon is discussed in detail, together with its implications for double beta decay searches in NEXT. The activity is measured through the alpha production rate induced in the fiducial volume by Rn-222 and its alpha-emitting progeny. The specific activity is measured to be (38.1 +/- 2.2 (stat.) +/- 5.9 (syst.)) mBq/m(3). Radon-induced electrons have also been characterized from the decay of the Bi-214 daughter ions plating out on the cathode of the time projection chamber. From our studies, we conclude that radon-induced backgrounds are sufficiently low to enable a successful NEXT-100 physics program, as the projected rate contribution should not exceed 0.1 counts/yr in the neutrinoless double beta decay sample.
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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(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(Cebrian, S. et al), Perez, J., Alvarez, V., Benlloch-Rodriguez, J., Botas, A., Carcel, S., et al. (2017). Radiopurity assessment of the energy readout for the NEXT double beta decay experiment. J. Instrum., 12, T08003–20pp.
Abstract: The “Neutrino Experiment with a Xenon Time-Projection Chamber” (NEXT) experiment intends to investigate the neutrinoless double beta decay of Xe-136, and therefore requires a severe suppression of potential backgrounds. An extensive material screening and selection process was undertaken to quantify the radioactivity of the materials used in the experiment. Separate energy and tracking readout planes using different sensors allow us to combine the measurement of the topological signature of the event for background discrimination with the energy resolution optimization. The design of radiopure readout planes, in direct contact with the gas detector medium, was especially challenging since the required components typically have activities too large for experiments demanding ultra-low background conditions. After studying the tracking plane, here the radiopurity control of the energy plane is presented, mainly based on gamma-ray spectroscopy using ultra-low background germanium detectors at the Laboratorio Subterraneo de Canfranc (Spain). All the available units of the selected model of photomultiplier have been screened together with most of the components for the bases, enclosures and windows. According to these results for the activity of the relevant radioisotopes, the selected components of the energy plane would give a contribution to the overall background level in the region of interest of at most 2.4 x 10(-4) counts keV(-1) kg(-1) y(-1), satisfying the sensitivity requirements of the NEXT experiment.
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NEXT Collaboration(Simon, A. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2017). Application and performance of an ML-EM algorithm in NEXT. J. Instrum., 12, P08009–22pp.
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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