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.

Abstract: Studying atmospheric neutrino oscillations in the few-GeV range with a multimegaton detector promises to determine the neutrino mass hierarchy. This is the main science goal pursued by the future KM3NeT/ORCA water Cherenkov detector in the Mediterranean Sea. In this paper, the processes that limit the obtainable resolution in both energy and direction in charged-current neutrino events in the ORCA detector are investigated. These processes include the composition of the hadronic fragmentation products, the subsequent particle propagation and the photon-sampling fraction of the detector. GEANT simulations of neutrino interactions in seawater produced by GENIE are used to study the effects in the 1-20 GeV range. It is found that fluctuations in the hadronic cascade in conjunction with the variation of the inelasticity y are most detrimental to the resolutions. The effect of limited photon sampling in the detector is of significantly less importance. These results will therefore also be applicable to similar detectors/media, such as those in ice.

Abstract: BRIKEN is a complex detection system to be installed at the RIB-facility of the RIKEN Nishina Center. It is aimed at the detection of heavy-ion implants, β-particles, γ-rays and β-delayed neutrons. The whole detection setup involves the Advanced Implantation Detection Array (AIDA), two HPGe Clover detectors and a large set of 166 counters of 3He embedded in a high-density polyethylene matrix. This article reports on a novel methodology developed for the conceptual design and optimisation of the 3He-tubes array, aiming at the best possible performance in terms of neutron detection. The algorithm is based on a geometric representation of two selected parameters of merit, namely, average neutron detection efficiency and efficiency flatness, as a function of a reduced number of geometric variables. The response of the detection system itself, for each configuration, is obtained from a systematic MC-simulation implemented realistically in Geant4. This approach has been found to be particularly useful. On the one hand, due to the different types and large number of 3He-tubes involved and, on the other hand, due to the additional constraints introduced by the ancillary detectors for charged particles and gamma-rays. Empowered by the robustness of the algorithm, we have been able to design a versatile detection system, which can be easily re-arranged into a compact mode in order to maximize the neutron detection performance, at the cost of the gamma-ray sensitivity. In summary, we have designed a system which shows, for neutron energies up to 1(5) MeV, a rather flat and high average efficiency of 68.6%(64%) and 75.7%(71%) for the hybrid and compact modes, respectively. The performance of the BRIKEN system has been also quantified realistically by means of MC-simulations made with different neutron energy distributions.