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), 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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