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(Novella, P. et al), Palmeiro, B., Sorel, M., Uson, A., Alvarez, V., Benlloch-Rodriguez, J. M., et al. (2019). Radiogenic backgrounds in the NEXT double beta decay experiment. J. High Energy Phys., 10(10), 051–26pp.
Abstract: Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterraneo de Canfranc with xenon depleted in Xe-136 are analyzed to derive a total background rate of (0.84 +/- 0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of Co-60, K-40, Bi-214 and Tl-208 to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25 +/- 0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5 sigma after 1 year of data taking. The background measurement in a Q(beta beta)+/- 100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75 +/- 0.12) events.
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NEXT Collaboration(Renner, J. et al), Kekic, M., Martinez-Lema, G., Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., et al. (2019). Energy calibration of the NEXT-White detector with 1% resolution near Q(beta beta) of Xe-136. J. High Energy Phys., 10(10), 230–13pp.
Abstract: Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (beta beta 0 nu), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for beta beta 0 nu searches.
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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(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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