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Dong, P. V., Huong, D. T., Queiroz, F. S., Valle, J. W. F., & Vaquera-Araujo, C. A. (2018). The dark side of flipped trinification. J. High Energy Phys., 04(4), 143–31pp.
Abstract: We propose a model which unifies the Left-Right symmetry with the SU(3)L gauge group, called flipped trinification, and based on the SU(3)(C)circle times SU(3)(L)circle times SU(3)(R)circle times U(1)(x) gauge group. The model inherits the interesting features of both symmetries while elegantly explaining the origin of the matter parity, W-p = ( 1)(3(B-L)+/- 2s), and dark matter stability. We develop the details of the spontaneous symmetry breaking mechanism in the model, determining the relevant mass eigenstates, and showing how neutrino masses are easily generated via the seesaw mechanism. Moreover, we introduce viable dark matter candidates, encompassing a fermion, scalar and possibly vector fields, leading to a potentially novel dark matter phenomenology.
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ATLAS and CMS Collaborations(Aaboud, M. et al), Alvarez Piqueras, D., Barranco Navarro, L., Cabrera Urban, S., Castillo Gimenez, V., Cerda Alberich, L., et al. (2018). Combination of inclusive and differential t(t)over-bar charge asymmetry measurements using ATLAS and CMS data at root S =7 and 8 TeV. J. High Energy Phys., 04(4), 033–68pp.
Abstract: This paper presents combinations of inclusive and differential measurements of the charge asymmetry (A(C)) in top quark pair (t(t)over-bar) events with a lepton+jets signature by the ATLAS and CMS Collaborations, using data from LHC proton-proton collisions at centre-of-mass energies of 7 and 8 TeV. The data correspond to integrated luminosities of about 5 and 20 fb(-1) for each experiment, respectively. The resulting combined LHC measurements of the inclusive charge asymmetry are A(C)(LHC7) = 0.005 +/- 0.007 (stat) +/- 0.006 (syst) at 7 TeV and A(C)(LHC8) = 0.0055 +/- 0.0023 (stat) +/- 0.0025 (syst) at 8 TeV. These values, as well as the combination of A(C )measurements as a function of the invariant mass of the t(t)over-bar system at 8 TeV, are consistent with the respective standard model predictions.
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Fabbri, A., & Pavloff, N. (2018). Momentum correlations as signature of sonic Hawking radiation in Bose-Einstein condensates. SciPost Phys., 4(4), 019–45pp.
Abstract: We study the two-body momentum correlation signal in a quasi one dimensional Bose-Einstein condensate in the presence of a sonic horizon. We identify the relevant correlation lines in momentum space and compute the intensity of the corresponding signal. We consider a set of different experimental procedures and identify the specific issues of each measuring process. We show that some inter-channel correlations, in particular the Hawking quantum-partner one, are particularly well adapted for witnessing quantum non-separability, being resilient to the effects of temperature and/or quantum quenches.
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Vento, V. (2018). Ions, Protons, and Photons as Signatures of Monopoles. Universe, 4(11), 117–12pp.
Abstract: Magnetic monopoles have been a subject of interest since Dirac established the relationship between the existence of monopoles and charge quantization. The Dirac quantization condition bestows the monopole with a huge magnetic charge. The aim of this study was to determine whether this huge magnetic charge allows monopoles to be detected by the scattering of charged ions and protons on matter where they might be bound. We also analyze if this charge favors monopolium (monopole-antimonopole) annihilation into many photons over two photon decays.
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Caron, S., Gomez-Vargas, G. A., Hendriks, L., & Ruiz de Austri, R. (2018). Analyzing gamma rays of the Galactic Center with deep learning. J. Cosmol. Astropart. Phys., 05(5), 058–24pp.
Abstract: We present the application of convolutional neural networks to a particular problem in gamma ray astronomy. Explicitly, we use this method to investigate the origin of an excess emission of GeV gamma rays in the direction of the Galactic Center, reported by several groups by analyzing Fermi-LAT data. Interpretations of this excess include gamma rays created by the annihilation of dark matter particles and gamma rays originating from a collection of unresolved point sources, such as millisecond pulsars. We train and test convolutional neural networks with simulated Fermi-LAT images based on point and diffuse emission models of the Galactic Center tuned to measured gamma ray data. Our new method allows precise measurements of the contribution and properties of an unresolved population of gamma ray point sources in the interstellar diffuse emission model. The current model predicts the fraction of unresolved point sources with an error of up to 10% and this is expected to decrease with future work.
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