Barenboim, G., Martinez-Mirave, P., Ternes, C. A., & Tortola, M. (2020). Sterile neutrinos with altered dispersion relations revisited. J. High Energy Phys., 03(3), 070–18pp.
Abstract: In this paper we investigate neutrino oscillations with altered dispersion relations in the presence of sterile neutrinos. Modified dispersion relations represent an agnostic way to parameterize new physics. Models of this type have been suggested to explain global neutrino oscillation data, including deviations from the standard three-neutrino paradigm as observed by a few experiments. We show that, unfortunately, in this type of models new tensions arise turning them incompatible with global data.
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de Salas, P. F., Forero, D. V., Gariazzo, S., Martinez-Mirave, P., Mena, O., Ternes, C. A., et al. (2021). 2020 global reassessment of the neutrino oscillation picture. J. High Energy Phys., 02(2), 071–36pp.
Abstract: We present an updated global fit of neutrino oscillation data in the simplest three-neutrino framework. In the present study we include up-to-date analyses from a number of experiments. Concerning the atmospheric and solar sectors, besides the data considered previously, we give updated analyses of IceCube DeepCore and Sudbury Neutrino Observatory data, respectively. We have also included the latest electron antineutrino data collected by the Daya Bay and RENO reactor experiments, and the long-baseline T2K and NO nu A measurements, as reported in the Neutrino 2020 conference. All in all, these new analyses result in more accurate measurements of theta (13), theta (12), Delta m212 and Delta m312. The best fit value for the atmospheric angle theta (23) lies in the second octant, but first octant solutions remain allowed at similar to 2.4 sigma. Regarding CP violation measurements, the preferred value of delta we obtain is 1.08 pi (1.58 pi) for normal (inverted) neutrino mass ordering. The global analysis still prefers normal neutrino mass ordering with 2.5 sigma statistical significance. This preference is milder than the one found in previous global analyses. These new results should be regarded as robust due to the agreement found between our Bayesian and frequentist approaches. Taking into account only oscillation data, there is a weak/moderate preference for the normal neutrino mass ordering of 2.00 sigma. While adding neutrinoless double beta decay from the latest Gerda, CUORE and KamLAND-Zen results barely modifies this picture, cosmological measurements raise the preference to 2.68 sigma within a conservative approach. A more aggressive data set combination of cosmological observations leads to a similar preference for normal with respect to inverted mass ordering, namely 2.70 sigma. This very same cosmological data set provides 2 sigma upper limits on the total neutrino mass corresponding to Sigma m(nu)< 0.12 (0.15) eV in the normal (inverted) neutrino mass ordering scenario. The bounds on the neutrino mixing parameters and masses presented in this up-to-date global fit analysis include all currently available neutrino physics inputs.
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Dev, A., Machado, P. A. N., & Martinez-Mirave, P. (2021). Signatures of ultralight dark matter in neutrino oscillation experiments. J. High Energy Phys., 01(1), 094–23pp.
Abstract: We study how neutrino oscillations could probe the existence of ultralight bosonic dark matter. Three distinct signatures on neutrino oscillations are identified, depending on the mass of the dark matter and the specific experimental setup. These are time modulation signals, oscillation probability distortions due to fast modulations, and fast varying matter effects. We provide all the necessary information to perform a bottom-up, model-independent experimental analysis to probe such scenarios. Using the future DUNE experiment as an example, we estimate its sensitivity to ultralight scalar dark matter. Our results could be easily used by any other oscillation experiment.
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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). Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC. Eur. Phys. J. C, 82(7), 618–29pp.
Abstract: DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 x 6 x 6 m(3) liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.
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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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