DUNE Collaboration(Abi, B. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2020). Neutrino interaction classification with a convolutional neural network in the DUNE far detector. Phys. Rev. D, 102(9), 092003–20pp.
Abstract: The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.
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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). Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector. Phys. Rev. D, 107(9), 092012–22pp.
Abstract: Measurements of electrons from ?e interactions are crucial for the Deep Underground Neutrino Experiment (DUNE) neutrino oscillation program, as well as searches for physics beyond the standard model, supernova neutrino detection, and solar neutrino measurements. This article describes the selection and reconstruction of low-energy (Michel) electrons in the ProtoDUNE-SP detector. ProtoDUNE-SP is one of the prototypes for the DUNE far detector, built and operated at CERN as a charged particle test beam experiment. A sample of low-energy electrons produced by the decay of cosmic muons is selected with a purity of 95%. This sample is used to calibrate the low-energy electron energy scale with two techniques. An electron energy calibration based on a cosmic ray muon sample uses calibration constants derived from measured and simulated cosmic ray muon events. Another calibration technique makes use of the theoretically well-understood Michel electron energy spectrum to convert reconstructed charge to electron energy. In addition, the effects of detector response to low-energy electron energy scale and its resolution including readout electronics threshold effects are quantified. Finally, the relation between the theoretical and reconstructed low-energy electron energy spectra is derived, and the energy resolution is characterized. The low-energy electron selection presented here accounts for about 75% of the total electron deposited energy. After the addition of lost energy using a Monte Carlo simulation, the energy resolution improves from about 40% to 25% at 50 MeV. These results are used to validate the expected capabilities of the DUNE far detector to reconstruct low-energy electrons.
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Ternes, C. A., Gariazzo, S., Hajjar, R., Mena, O., Sorel, M., & Tortola, M. (2019). Neutrino mass ordering at DUNE: An extra nu bonus. Phys. Rev. D, 100(9), 093004–10pp.
Abstract: We study the possibility of extracting the neutrino mass ordering at the future Deep Underground Neutrino Experiment using atmospheric neutrinos, which will be available before the muon neutrino beam starts being operational. The large statistics of the atmospheric muon neutrino and antineutrino samples at the far detector, together with the baselines of thousands of kilometers that these atmospheric (anti) neutrinos travel, provide ideal ingredients to extract the neutrino mass ordering via matter effects in the neutrino propagation through Earth. Crucially, muon capture by argon provides excellent charge tagging, allowing us to disentangle the neutrino and antineutrino signature. This is an important extra benefit of having a liquid argon time projection chamber as a far detector, that could render an similar to 3.5 sigma extraction of the mass ordering after approximately 7 yr of exposure.
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Fernandez-Martinez, E., Giordano, G., Mena, O., & Mocioiu, I. (2010). Atmospheric neutrinos in ice and measurement of neutrino oscillation parameters. Phys. Rev. D, 82(9), 093011–7pp.
Abstract: The main goal of the IceCube Deep Core array is to search for neutrinos of astrophysical origins. Atmospheric neutrinos are commonly considered as a background for these searches. We show that the very high statistics atmospheric neutrino data can be used to obtain precise measurements of the main oscillation parameters.
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Di Valentino, E., Melchiorri, A., Mena, O., & Vagnozzi, S. (2020). Interacting dark energy in the early 2020s: A promising solution to the H-0 and cosmic shear tensions. Phys. Dark Universe, 30, 100666–12pp.
Abstract: We examine interactions between dark matter and dark energy in light of the latest cosmological observations, focusing on a specific model with coupling proportional to the dark energy density. Our data includes Cosmic Microwave Background (CMB) measurements from the Planck 2018 legacy data release, late-time measurements of the expansion history from Baryon Acoustic Oscillations (BAO) and Supernovae Type Ia (SNeIa), galaxy clustering and cosmic shear measurements from the Dark Energy Survey Year 1 results, and the 2019 local distance ladder measurement of the Hubble constant H-0 from the Hubble Space Telescope. Considering Planck data both in combination with BAO or SNeIa data reduces the H-0 tension to a level which could possibly be compatible with a statistical fluctuation. The very same model also significantly reduces the Omega(m) – sigma(8) tension between CMB and cosmic shear measurements. Interactions between the dark sectors of our Universe remain therefore a promising joint solution to these persisting cosmological tensions.
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