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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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Oset, E., Ramos, A., Garzon, E. J., Molina, R., Tolos, L., Xiao, C. W., et al. (2012). Interaction of vector mesons with baryons and nuclei. Int. J. Mod. Phys. E, 21(11), 1230011–18pp.
Abstract: After some short introductory remarks on particular issues on the vector mesons in nuclei, in this paper, we present a short review of recent developments concerning the interaction of vector mesons with baryons and with nuclei from a modern perspective using the local hidden gauge formalism for the interaction of vector mesons. We present results for the vector-baryon interaction and in particular for the resonances which appear as composite states, dynamically generated from the interaction of vector mesons with baryons, taking also the mixing of these states with pseudoscalars and baryons into account. We then venture into the charm sector, reporting on hidden charm baryon states around 4400 MeV, generated from the interaction of vector mesons and baryons with charm, which have a strong repercussion on the properties of the J/Psi N interaction. We also address the interaction of K* with nuclei and make suggestions to measure the predicted huge width in the medium by means of transparency ratio. The formalism is extended to study the phenomenon of J/psi suppression in nuclei via J/psi photo-production reactions.
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Valle, J. W. F. (2015). Status and implications of neutrino masses: a brief panorama. Int. J. Mod. Phys. A, 30(13), 1530034–13pp.
Abstract: With the historic discovery of the Higgs boson our picutre of particle physics would have been complete were it nor for the neutrino sector and cosmology. I briefly discuss the role of neutrino masses and mixing upon gauge coupling unification, electroweak breaking and the flavor sector. Time is ripe for new discoveries such as leptonic CP violation, charged lepton flavor violation and neutrinoless double beta decay. Neutrinos could also play a role is elucidating the nature of dark matter and cosmic inflation.
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Araujo Filho, A. A., Reis, J. A. A. S., & Ghosh, S. (2023). Quantum gases on a torus. Int. J. Geom. Methods Mod. Phys., 20(10), 2350178–19pp.
Abstract: This paper is aimed at studying the thermodynamic properties of quantum gases confined to a torus. To do that, we consider noninteracting gases within the grand canonical ensemble formalism. In this context, fermions and bosons are taken into account and the calculations are properly provided in both analytical and numerical manners. In particular, the system turns out to be sensitive to the topological parameter under consideration: the winding number. Furthermore, we also derive a model in order to take into account interacting quantum gases. To corroborate our results, we implement such a method for two different scenarios: a ring and a torus.
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Aliaga, R. J. (2017). Real-Time Estimation of Zero Crossings of Sampled Signals for Timing Using Cubic Spline Interpolation. IEEE Trans. Nucl. Sci., 64(8), 2414–2422.
Abstract: A scheme is proposed for hardware estimation of the location of zero crossings of sampled signals with subsample resolution for timing applications, which consists of interpolating the signal with a cubic spline near the zero crossing and then finding the root of the resulting polynomial. An iterative algorithm based on the bisection method is presented that obtains one bit of the result per step and admits an efficient digital implementation using fixed-point representation. In particular, the root estimation iteration involves only two additions, and the initial values can be obtained from finite impulse response (FIR) filters with certain symmetry properties. It is shown that this allows online real-time estimation of timestamps in free-running sampling detector systems with improved accuracy with respect to the more common linear interpolation. The method is evaluated with simulations using ideal and real timing signals, and estimates are given for the resource usage and speed of its implementation.
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Carrio, F. (2022). The Data Acquisition System for the ATLAS Tile Calorimeter Phase-II Upgrade Demonstrator. IEEE Trans. Nucl. Sci., 69(4), 687–695.
Abstract: The tile calorimeter (TileCal) is the central hadronic calorimeter of the ATLAS experiment at the large hadron collider (LHC). In 2025, the LHC will be upgraded leading to the high luminosity LHC (HL-LHC). The HL-LHC will deliver an instantaneous luminosity up to seven times larger than the LHC nominal luminosity. The ATLAS Phase-II upgrade (2025-2027) will accommodate the subdetectors to the HL-LHC requirements. As part of this upgrade, the majority of the TileCal on-detector and off-detector electronics will be replaced using a new readout strategy, where the on-detector electronics will digitize and transmit digitized detector data to the off-detector electronics at the bunch crossing frequency (40 MHz). In the counting rooms, the off-detector electronics will compute reconstructed trigger objects for the first-level trigger and will store the digitized samples in pipelined buffers until the reception of a trigger acceptance signal. The off-detector electronics will also distribute the LHC clock to the on-detector electronics embedded within the digital data stream. The TileCal Phase-II upgrade project has undertaken an extensive research and development program that includes the development of a Demonstrator module to evaluate the performance of the new clock and readout architecture envisaged for the HL-LHC. The Demonstrator module equipped with the latest version of the on-detector electronics was built and inserted into the ATLAS experiment. The Demonstrator module is operated and read out using a Tile PreProcessor (TilePPr) Demonstrator which enables backward compatibility with the present ATLAS Trigger and Data AcQuisition (TDAQ), and the timing, trigger, and command (TTC) systems. This article describes in detail the main hardware and firmware components of the clock distribution and data acquisition systems for the Demonstrator module, focusing on the TilePPr Demonstrator.
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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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Bazeia, D., Losano, L., Olmo, G. J., & Rubiera-Garcia, D. (2017). Geodesically complete BTZ-type solutions of 2+1 Born-Infeld gravity. Class. Quantum Gravity, 34(4), 045006–21pp.
Abstract: We study Born-Infeld gravity coupled to a static, non-rotating electric field in 2 + 1 dimensions and find exact analytical solutions. Two families of such solutions represent geodesically complete, and hence nonsingular, spacetimes. Another family represents a point-like charge with a singularity at the center. Despite the absence of rotation, these solutions resemble the charged, rotating BTZ solution of general relativity but with a richer structure in terms of horizons. The nonsingular character of the first two families turn out to be attached to the emergence of a wormhole structure on their innermost region. This seems to be a generic prediction of extensions of general relativity formulated in metric-affine (or Palatini) spaces, where metric and connection are regarded as independent degrees of freedom.
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Mendoza, S., & Olmo, G. J. (2015). Astrophysical constraints and insights on extended relativistic gravity. Astrophys. Space Sci., 357(2), 133–6pp.
Abstract: We give precise details to support that observations of gravitational lensing at scales of individual, groups and clusters of galaxies can be understood in terms of nonNewtonian gravitational interactions with a relativistic structure compatible with the Einstein Equivalence Principle. This result is derived on very general grounds without knowing the underlying structure of the gravitational field equations. As such, any developed gravitational theory built to deal with these astrophysical scales needs to reproduce the obtained results of this article.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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