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NEXT Collaboration(Serra, L. et al), Sorel, M., Alvarez, V., Carcel, S., Cervera-Villanueva, A., Diaz, J., et al. (2015). An improved measurement of electron-ion recombination in high-pressure xenon gas. J. Instrum., 10, P03025–21pp.
Abstract: We report on results obtained with the NEXT-DEMO prototype of the NEXT-100 high-pressure xenon gas time projection chamber (TPC), filled with pure xenon gas at 10 bar pressure and exposed to an alpha decay calibration source. Compared to our previous measurements with alpha particles, an upgraded detector and improved analysis techniques have been used. We measure event-by-event correlated fluctuations between ionization and scintillation due to electronion recombination in the gas, with correlation coefficients between -0.80 and -0.56 depending on the drift field conditions. By combining the two signals, we obtain a 2.8% FWHM energy resolution for 5.49 MeV alpha particles and a measurement of the optical gain of the electroluminescent TPC. The improved energy resolution also allows us to measure the specific activity of the radon in the gas due to natural impurities. Finally, we measure the average ratio of excited to ionized atoms produced in the xenon gas by alpha particles to be 0.561 +/- 0.045, translating into an average energy to produce a primary scintillation photon of W-ex = (39.2 +/- 3.2) eV.
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NEXT Collaboration(Cebrian, S. et al), Alvarez, V., Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., et al. (2015). Radiopurity assessment of the tracking readout for the NEXT double beta decay experiment. J. Instrum., 10, P05006–16pp.
Abstract: The “Neutrino Experiment with a Xenon Time-Projection Chamber” (NEXT) is intended to investigate the neutrinoless double beta decay of Xe-136, which requires a severe suppression of potential backgrounds; therefore, an extensive screening and selection process is underway to control the radiopurity levels of the materials to be used in the experimental set-up of NEXT. The detector design combines the measurement of the topological signature of the event for background discrimination with the energy resolution optimization. Separate energy and tracking readout planes are based on different sensors: photomultiplier tubes for calorimetry and silicon multi-pixel photon counters for tracking. The design of a radiopure tracking plane, in direct contact with the gas detector medium, was specially challenging since the needed components like printed circuit boards, connectors, sensors or capacitors have typically, according to available information in databases and in the literature, activities too large for experiments requiring ultra-low background conditions. Here, the radiopurity assessment of tracking readout components based on gamma-ray spectroscopy using ultra-low background germanium detectors at the Laboratorio Subterraneo de Canfranc (Spain) is described. According to the obtained results, radiopure enough printed circuit boards made of kapton and copper, silicon photomultipliers and other required components, fulfilling the requirement of an overall background level in the region of interest of at most 8 x 10(-4) counts keV(-1) kg(-1) y(-1), have been identified.
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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.
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NEXT Collaboration(Martin-Albo, J. et al), Muñoz Vidal, J., Ferrario, P., Nebot-Guinot, M., Gomez-Cadenas, J. J., Alvarez, V., et al. (2016). Sensitivity of NEXT-100 to neutrinoless double beta decay. J. High Energy Phys., 05(5), 159–30pp.
Abstract: NEXT-100 is an electroluminescent high-pressure xenon gas time projection chamber that will search for the neutrinoless double beta (0v beta beta) decay of Xe-136. The detector possesses two features of great value for 0v beta beta searches: energy resolution better than 1% FWHM at the Q value of Xe-136 and track reconstruction for the discrimination of signal and background events. This combination results in excellent sensitivity, as discussed in this paper. Material-screening measurements and a detailed Monte Carlo detector simulation predict a background rate for NEXT-100 of at most 4 x 10(-4) counts keV(-1) kg(-1) yr(-1). Accordingly, the detector will reach a sensitivity to the 0v beta beta-decay half-life of 2.8 x 10(25) years (90% CL) for an exposure of 100 kg.year, or 6.0 x 10(25) years after a run of 3 effective years.
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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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