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Centelles Chulia, S., Cepedello, R., & Medina, O. (2022). Absolute neutrino mass scale and dark matter stability from flavour symmetry. J. High Energy Phys., 10(10), 080–23pp.
Abstract: We explore a simple but extremely predictive extension of the scotogenic model. We promote the scotogenic symmetry Z(2) to the flavour non-Abelian symmetry sigma(81), which can also automatically protect dark matter stability. In addition, sigma(81) leads to striking predictions in the lepton sector: only Inverted Ordering is realised, the absolute neutrino mass scale is predicted to be m(lightest)approximate to 7.5x10(-4) eV and the Majorana phases are correlated in such a way that vertical bar m(ee)vertical bar approximate to 0.018 eV. The model also leads to a strong correlation between the solar mixing angle theta(12) and delta(CP), which may be falsified by the next generation of neutrino oscillation experiments. The setup is minimal in the sense that no additional symmetries or flavons are required.
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Pompa, F., Capozzi, F., Mena, O., & Sorel, M. (2022). Absolute nu Mass Measurement with the DUNE Experiment. Phys. Rev. Lett., 129(12), 121802–6pp.
Abstract: Time of flight delay in the supernova neutrino signal offers a unique tool to set model-independent constraints on the absolute neutrino mass. The presence of a sharp time structure during a first emission phase, the so-called neutronization burst in the electron neutrino flavor time distribution, makes this channel a very powerful one. Large liquid argon underground detectors will provide precision measurements of the time dependence of the electron neutrino fluxes. We derive here a new v mass sensitivity attainable at the future DUNE far detector from a future supernova collapse in our galactic neighborhood, finding a sub-eV reach under favorable scenarios. These values are competitive with those expected for laboratory direct neutrino mass searches.
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Delhom, A., Macedo, C. F. B., Olmo, G. J., & Crispino, L. C. B. (2019). Absorption by black hole remnants in metric-affine gravity. Phys. Rev. D, 100(2), 024016–12pp.
Abstract: Using numerical methods, we investigate the absorption properties of a family of nonsingular solutions which arise in different metric-affine theories, such as quadratic and Born-Infeld gravity. These solutions continuously interpolate between Schwarzschild black holes and naked solitons with wormhole topology. The resulting spectrum is characterized by a series of quasibound states excitations, associated with the existence of a stable photonsphere.
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Olmo, G. J., Rubiera-Garcia, D., & Sanchez-Puente, A. (2018). Accelerated observers and the notion of singular spacetime. Class. Quantum Gravity, 35(5), 055010–18pp.
Abstract: Geodesic completeness is typically regarded as a basic criterion to determine whether a given spacetime is regular or singular. However, the principle of general covariance does not privilege any family of observers over the others and, therefore, observers with arbitrary motions should be able to provide a complete physical description of the world. This suggests that in a regular spacetime, all physically acceptable observers should have complete paths. In this work we explore this idea by studying the motion of accelerated observers in spherically symmetric spacetimes and illustrate it by considering two geodesically complete black hole spacetimes recently described in the literature. We show that for bound and locally unbound accelerations, the paths of accelerated test particles are complete, providing further support to the regularity of such spacetimes.
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Begone, G., Deisenroth, M. P., Kim, J. S., Liem, S., Ruiz de Austri, R., & Welling, M. (2019). Accelerating the BSM interpretation of LHC data with machine learning. Phys. Dark Universe, 24, 100293–5pp.
Abstract: The interpretation of Large Hadron Collider (LHC) data in the framework of Beyond the Standard Model (BSM) theories is hampered by the need to run computationally expensive event generators and detector simulators. Performing statistically convergent scans of high-dimensional BSM theories is consequently challenging, and in practice unfeasible for very high-dimensional BSM theories. We present here a new machine learning method that accelerates the interpretation of LHC data, by learning the relationship between BSM theory parameters and data. As a proof-of-concept, we demonstrate that this technique accurately predicts natural SUSY signal events in two signal regions at the High Luminosity LHC, up to four orders of magnitude faster than standard techniques. The new approach makes it possible to rapidly and accurately reconstruct the theory parameters of complex BSM theories, should an excess in the data be discovered at the LHC.
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