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de Salas, P. F., Gariazzo, S., Mena, O., Ternes, C. A., & Tortola, M. (2018). Neutrino Mass Ordering From Oscillations and Beyond: 2018 Status and Future Prospects. Front. Astron. Space Sci., 5, 36–50pp.
Abstract: The ordering of the neutrino masses is a crucial input for a deep understanding of flavor physics, and its determination may provide the key to establish the relationship among the lepton masses and mixings and their analogous properties in the quark sector. The extraction of the neutrino mass ordering is a data-driven field expected to evolve very rapidly in the next decade. In this review, we both analyse the present status and describe the physics of subsequent prospects. Firstly, the different current available tools to measure the neutrino mass ordering are described. Namely, reactor, long-baseline (accelerator and atmospheric) neutrino beams, laboratory searches for beta and neutrinoless double beta decays and observations of the cosmic background radiation and the large scale structure of the universe are carefully reviewed. Secondly, the results from an up-to-date comprehensive global fit are reported: the Bayesian analysis to the 2018 publicly available oscillation and cosmological data sets provides strong evidence for the normal neutrino mass ordering vs. the inverted scenario, with a significance of 3.5 standard deviations. This preference for the normal neutrino mass ordering is mostly due to neutrino oscillation measurements. Finally, we shall also emphasize the future perspectives for unveiling the neutrinomass ordering. In this regard, apart from describing the expectations from the aforementioned probes, we also focus on those arising from alternative and novel methods, as 21 cm cosmology, core-collapse supernova neutrinos and the direct detection of relic neutrinos.
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Folgado, M. G., & Sanz, V. (2022). Exploring the political pulse of a country using data science tools. J. Comput. Soc. Sci., 5, 987–1000.
Abstract: In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis.
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Fornengo, N., Lineros, R. A., Regis, M., & Taoso, M. (2012). Galactic synchrotron emission from WIMPs at radio frequencies. J. Cosmol. Astropart. Phys., 01(1), 005–25pp.
Abstract: Dark matter annihilations in the Galactic halo inject relativistic electrons and positrons which in turn generate a synchrotron radiation when interacting with the galactic magnetic field. We calculate the synchrotron flux for various dark matter annihilation channels, masses, and astrophysical assumptions in the low-frequency range and compare our results with radio surveys from 22 MHz to 1420 MHz. We find that current observations are able to constrain particle dark matter with “thermal” annihilation cross-sections, i.e. (sigma v) = 3 x 10(-26) cm(3) s(-1); and masses M-DM less than or similar to 10 GeV. We discuss the dependence of these bounds on the astrophysical assumptions, namely galactic dark matter distribution, cosmic rays propagation parameters, and structure of the galactic magnetic field. Prospects for detection in future radio surveys are outlined.
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