De Romeri, V., Hirsch, M., & Malinsky, M. (2011). Soft masses in supersymmetric SO(10) GUTs with low intermediate scales. Phys. Rev. D, 84(5), 053012–15pp.
Abstract: The specific shape of the squark, slepton and gaugino mass spectra, if measured with sufficient accuracy, can provide invaluable information not only about the dynamics underpinning their origin at some very high scale such as the unification scale M(G), but also about the intermediate scale physics encountered throughout their renormalization group equations evolution down to the energy scale accessible for the LHC. In this work, we study general features of the TeV scale soft supersymmetry breaking parameters stemming from a generic mSugra configuration within certain classes of supersymmetry SO(10) GUTs with different intermediate symmetries below M(G). We show that particular combinations of soft masses show characteristic deviations from the mSugra limit in different models and thus, potentially, allow to distinguish between these, even if the new intermediate scales are outside the energy range probed at accelerators. We also compare our results to those obtained for the three minimal seesaw models with mSugra boundary conditions and discuss the main differences between those and our SO(10) based models.
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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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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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De Romeri, V., Puerta, M., & Vicente, A. (2022). Dark matter in a charged variant of the Scotogenic model. Eur. Phys. J. C, 82(7), 623–16pp.
Abstract: Scotogenic models are among the most popular possibilities to link dark matter and neutrino masses. In this work we discuss a variant of the Scotogenic model that includes charged fermions and a doublet with hypercharge 3/2. Neutrino masses are induced at the one-loop level thanks to the states belonging to the dark sector. However, in contrast to the standard Scotogenic model, only the scalar dark matter candidate is viable in this version. After presenting the model and explaining some particularities about neutrino mass generation, we concentrate on its dark matter phenomenology. We show that the observed dark matter relic density can be correctly reproduced in the usual parameter space regions found for the standard Scotogenic model or the Inert Doublet model. In addition, the presence of the charged fermions opens up new viable regions, not present in the original scenarios, provided some tuning of the parameters is allowed.
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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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