The NEXT experiment aims to observe the neutrinoless double beta decay of 136Xe 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 Qββ. 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 22Na 1275 keV gammas and electronpositron pairs produced by conversions of gammas from the 228Th 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.[Figure not available: see fulltext.]

Abstract: NEXT-100 is an electroluminescent high-pressure xenon gas time projection chamber that will search for the neutrinoless double beta (0νββ) decay of 136Xe. The detector possesses two features of great value for 0νββ searches: energy resolution better than 1% FWHM at the Q value of 136Xe 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 × 10−4 counts keV−1 kg−1 yr−1. Accordingly, the detector will reach a sensitivity to the 0νββ-decay half-life of 2.8 × 1025 years (90% CL) for an exposure of 100 kg·year, or 6.0 × 1025 years after a run of 3 effective years.[Figure not available: see fulltext.]

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.

Single neutral pion production via muon antineutrino charged-current interactions in plastic scintillator (CH) is studied using the MINERvA detector exposed to the NuMI low-energy, wideband antineutrino beam at Fermilab. Measurement of this process constrains models of neutral pion production in nuclei, which is important because the neutral-current analog is a background for ν-e appearance oscillation experiments. The differential cross sections for π0 momentum and production angle, for events with a single observed π0 and no charged pions, are presented and compared to model predictions. These results comprise the first measurement of the π0 kinematics for this process.