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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(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(Renner, J. et al), Martinez-Lema, G., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2018). Initial results on energy resolution of the NEXT-White detector. J. Instrum., 13, P10020–14pp.
Abstract: One of the major goals of the NEXT-White (NEW) detector is to demonstrate the energy resolution that an electroluminescent high pressure xenon TPC can achieve for high energy tracks. For this purpose, energy calibrations with Cs-137 and Th-232 sources have been carried out as a part of the long run taken with the detector during most of 2017. This paper describes the initial results obtained with those calibrations, showing excellent linearity and an energy resolution that extrapolates to approximately 1% FWHM at Q(beta beta).
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NEXT Collaboration(Rogers, L. et al), Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., Carrion, J. V., et al. (2018). High voltage insulation and gas absorption of polymers in high pressure argon and xenon gases. J. Instrum., 13, P10002–19pp.
Abstract: High pressure gas time projection chambers (HPGTPCs) are made with a variety of materials, many of which still await proper characterization in high pressure noble gas environments. As HPGTPCs increase in size toward ton-scale detectors, assemblies become larger and more complex, creating a need for detailed understanding of how structural supports and high voltage insulators behave. This includes identification of materials with predictable mechanical properties and without surface charge accumulation that may lead to field deformation or sparking. This paper explores the mechanical and electrical effects of high pressure gas environments on insulating polymers PTFE, HDPE, PEEK, POM and UHMW in argon and xenon, including studying gas absorption, swelling and high voltage insulation strength.
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NEXT Collaboration(Simon, A. et al), Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., Lopez-March, N., et al. (2021). Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution. J. High Energy Phys., 07(7), 146–38pp.
Abstract: Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of similar to 10(27) yr, requiring suppressing backgrounds to < 1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of <similar to> 5 when reconstructing electron-positron pairs in the Tl-208 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterraneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of similar to 10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV e(-)e(+) pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.
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