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De Romeri, V., Giunti, C., Stuttard, T., & Ternes, C. A. (2023). Neutrino oscillation bounds on quantum decoherence. J. High Energy Phys., 09(9), 097–24pp.
Abstract: We consider quantum-decoherence effects in neutrino oscillation data. Working in the open quantum system framework we adopt a phenomenological approach that allows to parameterize the energy dependence of the decoherence effects. We consider several phenomenological models. We analyze data from the reactor experiments RENO, Daya Bay and KamLAND and from the accelerator experiments NOvA, MINOS/MINOS+ and T2K. We obtain updated constraints on the decoherence parameters quantifying the strength of damping effects, which can be as low as Gamma ij less than or similar to 8 x 10-27 GeV at 90% confidence level in some cases. We also present sensitivities for the future facilities DUNE and JUNO.
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Barenboim, G., Martinez-Mirave, P., Ternes, C. A., & Tortola, M. (2023). Neutrino CPT violation in the solar sector. Phys. Rev. D, 108(3), 035039–10pp.
Abstract: In this paper, we place new bounds on CPT violation in the solar neutrino sector analyzing the results from solar experiments and KamLAND. We also discuss the sensitivity of the next-generation experiments DUNE and Hyper-Kamiokande, which will provide accurate measurements of the solar neutrino oscillation parameters. The joint analysis of both experiments will further improve the precision due to cancellations in the systematic uncertainties regarding the solar neutrino flux. In combination with the next-generation reactor experiment JUNO, the bound on CPT violation in the solar sector could be improved by 1 order of magnitude in comparison with current constraints. The distinguishability among CPT-violating neutrino oscillations and neutrino nonstandard interactions in the solar sector is also addressed.
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DUNE Collaboration(Abud, A. A. et al), Amedo, P., Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., et al. (2023). Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora. Eur. Phys. J. C, 83(7), 618–25pp.
Abstract: The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 +/- 0.6% and 84.1 +/- 0.6%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation.
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Gariazzo, S., Gerbino, M., Brinckmann, T., Lattanzi, M., Mena, O., Schwetz, T., et al. (2022). Neutrino mass and mass ordering: no conclusive evidence for normal ordering. J. Cosmol. Astropart. Phys., 10(10), 010–18pp.
Abstract: The extraction of the neutrino mass ordering is one of the major challenges in particle physics and cosmology, not only for its implications for a fundamental theory of mass generation in nature, but also for its decisive role in the scale of future neutrinoless double beta decay experimental searches. It has been recently claimed that current oscillation, beta decay and cosmological limits on the different observables describing the neutrino mass parameter space provide robust decisive Bayesian evidence in favor of the normal ordering of the neutrino mass spectrum [1]. We further investigate these strong claims using a rich and wide phenomenology, with different sampling techniques of the neutrino parameter space. Contrary to the findings of Jimenez et al. [1], no decisive evidence for the normal mass ordering is found. Neutrino mass ordering analyses must rely on priors and parameterizations that are ordering-agnostic: robust results should be regarded as those in which the preference for the normal neutrino mass ordering is driven exclusively by the data, while we find a difference of up to a factor of 33 in the Bayes factors among the different priors and parameterizations exploited here. An ordering-agnostic prior would be represented by the case of parameterizations sampling over the two mass splittings and a mass scale, or those sampling over the individual neutrino masses via normal prior distributions only. In this regard, we show that the current significance in favor of the normal mass ordering should be taken as 2.7 sigma (i.e. moderate evidence), mostly driven by neutrino oscillation data. Let us stress that, while current data favor NO only mildly, we do not exclude the possibility that this may change in the future. Eventually, upcoming oscillation and cosmological data may (or may not) lead to a more significant exclusion of IO.
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DUNE Collaboration(Abud, A. A. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2022). Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. Eur. Phys. J. C, 82(10), 903–19pp.
Abstract: Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
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