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.

Abstract: We demonstrate that the application of an external magnetic field could lead to an improved background rejection in neutrinoless double-beta (0 nu beta beta) decay experiments using a high-pressure xenon (HPXe) TPC. HPXe chambers are capable of imaging electron tracks, a feature that enhances the separation between signal events (the two electrons emitted in the 0 nu beta beta decay of Xe-136) and background events, arising chiefly from single electrons of kinetic energy compatible with the end-point of the 0 nu beta beta decay (Q(beta beta)). Applying an external magnetic field of sufficiently high intensity (in the range of 0.5-1 Tesla for operating pressures in the range of 5-15 atmospheres) causes the electrons to produce helical tracks. Assuming the tracks can be properly reconstructed, the sign of the curvature can be determined at several points along these tracks, and such information can be used to separate signal (0 nu beta beta) events containing two electrons producing a track with two different directions of curvature from background (single-electron) events producing a track that should spiral in a single direction. Due to electron multiple scattering, this strategy is not perfectly efficient on an event-by-event basis, but a statistical estimator can be constructed which can be used to reject background events by one order of magnitude at a moderate cost (about 30%) in signal efficiency. Combining this estimator with the excellent energy resolution and topological signature identification characteristic of the HPXe TPC, it is possible to reach a background rate of less than one count per ton-year of exposure. Such a low background rate is an essential feature of the next generation of 0 nu beta beta experiments, aiming to fully explore the inverse hierarchy of neutrino masses.

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.