Ellis, J., Mavromatos, N. E., Sakharov, A. S., & Sarkisyan-Grinbaum, E. K. (2019). Limits on neutrino Lorentz violation from multimessenger observations of TXS 0506+056. Phys. Lett. B, 789, 352–355.
Abstract: The observation by the IceCube Collaboration of a high-energy (E greater than or similar to 200 TeV) neutrino from the direction of the blazar TXS 0506+056 and the coincident observations of enhanced gamma-ray emissions from the same object by MAGIC and other experiments can be used to set stringent constraints on Lorentz violation in the propagation of neutrinos that is linear in the neutrino energy: Delta v = -E/M-1, where Delta v is the deviation from the velocity of light, and M-1 is an unknown high energy scale to be constrained by experiment. Allowing for a difference in neutrino and photon propagation times of similar to 10 days, we find that M-1 greater than or similar to 3 x 10(16) GeV. This improves on previous limits on linear Lorentz violation in neutrino propagation by many orders of magnitude, and the same is true for quadratic Lorentz violation.
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n_TOF Collaboration(Lederer-Woods, C. et al), Domingo-Pardo, C., Tain, J. L., & Tarifeño-Saldivia, A. (2019). Measurement of Ge-73(n, gamma) cross sections and implications for stellar nucleosynthesis. Phys. Lett. B, 790, 458–465.
Abstract: Ge-73(n, gamma) cross sections were measured at the neutron time-of-flight facility n_TOF at CERN up to neutron energies of 300 keV, providing for the first time experimental data above 8 keV. Results indicate that the stellar cross section at kT = 30 keV is 1.5 to 1.7 times higher than most theoretical predictions. The new cross sections result in a substantial decrease of Ge-73 produced in stars, which would explain the low isotopic abundance of Ge-73 in the solar system.
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Ortiz Arciniega, J. L., Carrio, F., & Valero, A. (2019). FPGA implementation of a deep learning algorithm for real-time signal reconstruction in particle detectors under high pile-up conditions. J. Instrum., 14, P09002–13pp.
Abstract: The analog signals generated in the read-out electronics of particle detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital filters, which provides information about the energy deposited in the detector. The classical digital filters have a good performance in ideal situations with Gaussian electronic noise and no pulse shape distortion. However, high-energy particle colliders, such as the Large Hadron Collider (LHC) at CERN, can produce multiple simultaneous events, which produce signal pileup. The performance of classical digital filters deteriorates in these conditions since the signal pulse shape gets distorted. In addition, this type of experiments produces a high rate of collisions, which requires high throughput data acquisitions systems. In order to cope with these harsh requirements, new read-out electronics systems are based on high-performance FPGAs, which permit the utilization of more advanced real-time signal reconstruction algorithms. In this paper, a deep learning method is proposed for real-time signal reconstruction in high pileup particle detectors. The performance of the new method has been studied using simulated data and the results are compared with a classical FIR filter method. In particular, the signals and FIR filter used in the ATLAS Tile Calorimeter are used as benchmark. The implementation, resources usage and performance of the proposed Neural Network algorithm in FPGA are also presented.
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Centelles Chulia, S., & Trautner, A. (2020). Asymmetric tri-bi-maximal mixing and residual symmetries. Mod. Phys. Lett. A, 35(35), 2050292–15pp.
Abstract: Asymmetric tri-bi-maximal mixing is a recently proposed, grand unified theory (GUT) based, flavor mixing scheme. In it, the charged lepton mixing is fixed by the GUT connection to down-type quarks and a T-13 flavor symmetry, while neutrino mixing is assumed to be tri-bi-maximal (TBM) with one additional free phase. Here we show that this additional free phase can be fixed by the residual flavor and CP symmetries of the effective neutrino mass matrix. We discuss how those residual symmetries can be unified with T-13 and identify the smallest possible unified flavor symmetries, namely (Z(13)xZ(13))(sic)D-12 and (Z(13)xZ(13))(sic)S-4. Sharp predictions are obtained for lepton mixing angles, CP violating phases and neutrinoless double beta decay.
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NEXT Collaboration, Carcel, S., Carrion, J. V., Felkai, R., Kekic, M., Lopez-March, N., et al. (2020). Mitigation of backgrounds from cosmogenic Xe-137 in xenon gas experiments using He-3 neutron capture. J. Phys. G, 47(7), 075001–17pp.
Abstract: Xe-136 is used as the target medium for many experiments searching for 0 nu beta beta. Despite underground operation, cosmic muons that reach the laboratory can produce spallation neutrons causing activation of detector materials. A potential background that is difficult to veto using muon tagging comes in the form of Xe-137 created by the capture of neutrons on Xe-136. This isotope decays via beta decay with a half-life of 3.8 min and a Q(beta) of similar to 4.16 MeV. This work proposes and explores the concept of adding a small percentage of He-3 to xenon as a means to capture thermal neutrons and reduce the number of activations in the detector volume. When using this technique we find the contamination from Xe-137 activation can be reduced to negligible levels in tonne and multi-tonne scale high pressure gas xenon neutrinoless double beta decay experiments running at any depth in an underground laboratory.
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n_TOF Collaboration(Mazzone, A. et al), Babiano-Suarez, V., Caballero, L., Domingo-Pardo, C., Ladarescu, I., & Tain, J. L. (2020). Measurement of the Gd-154(n, gamma) cross section and its astrophysical implications. Phys. Lett. B, 804, 135405–6pp.
Abstract: The neutron capture cross section of Gd-154 was measured from 1 eV to 300 keV in the experimental area located 185 m from the CERN n_TOF neutron spallation source, using a metallic sample of gadolinium, enriched to 67% in Gd-154. The capture measurement, performed with four C6D6 scintillation detectors, has been complemented by a transmission measurement performed at the GELINA time-of-flight facility (JRC-Geel), thus minimising the uncertainty related to sample composition. An accurate Maxwellian averaged capture cross section (MACS) was deduced over the temperature range of interest for s process nucleosynthesis modelling. We report a value of 880(50) mb for the MACS at kT = 30 keV, significantly lower compared to values available in literature. The new adopted Gd-154(n, gamma) cross section reduces the discrepancy between observed and calculated solar s-only isotopic abundances predicted by s-process nucleosynthesis models.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2020). Performance of the ATLAS muon triggers in Run 2. J. Instrum., 15(9), P09015–57pp.
Abstract: The performance of the ATLAS muon trigger system is evaluated with proton-proton (pp) and heavy-ion (HI) collision data collected in Run 2 during 2015-2018 at the Large Hadron Collider. It is primarily evaluated using events containing a pair of muons from the decay of Z bosons to cover the intermediate momentum range between 26 GeV and 100 GeV. Overall, the efficiency of the single-muon triggers is about 68% in the barrel region and 85% in the endcap region. The p(T) range for efficiency determination is extended by using muons from decays of J/psi mesons, W bosons, and top quarks. The performance in HI collision data is measured and shows good agreement with the results obtained in pp collisions. The muon trigger shows uniform and stable performance in good agreement with the prediction of a detailed simulation. Dedicated multi-muon triggers with kinematic selections provide the backbone to beauty, quarkonia, and low-mass physics studies. The design, evolution and performance of these triggers are discussed in detail.
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Angles-Castillo, A., Perucho, M., Marti, J. M., & Laing, R. A. (2021). On the deceleration of Fanaroff-Riley Class I jets: mass loading of magnetized jets by stellar winds. Mon. Not. Roy. Astron. Soc., 500(1), 1512–1530.
Abstract: In this paper, we present steady-state relativistic magnetohydrodynamic simulations that include a mass-load term to study the process of jet deceleration. The mass load mimics the injection of a proton-electron plasma from stellar winds within the host galaxy into initially pair plasma jets, with mean stellar mass-losses ranging from 10(-14) to 10(-9) M-circle dot yr(-1). The spatial jet evolution covers similar to 500 pc from jet injection in the grid at 10 pc from the jet nozzle. Our simulations use a relativistic gas equation of state and a pressure profile for the ambient medium. We compare these simulations with previous dynamical simulations of relativistic, non-magnetized jets. Our results show that toroidal magnetic fields can prevent fast jet expansion and the subsequent embedding of further stars via magnetic tension. In this sense, magnetic fields avoid a runaway deceleration process. Furthermore, when the mass load is large enough to increase the jet density and produce fast, differential jet expansion, the conversion of magnetic energy flux into kinetic energy flux (i.e. magnetic acceleration), helps to delay the deceleration process with respect to non-magnetized jets. We conclude that the typical stellar population in elliptical galaxies cannot explain jet deceleration in classical Fanaroff-Riley type I radio galaxies. However, we observe a significant change in the jet composition, thermodynamical parameters, and energy dissipation along its evolution, even for moderate values of the mass load.
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Al Kharusi, S. et al, & Colomer, M. (2021). SNEWS 2.0: a next-generation supernova early warning system for multi-messenger astronomy. New J. Phys., 23(3), 031201–34pp.
Abstract: The next core-collapse supernova in the Milky Way or its satellites will represent a once-in-a-generation opportunity to obtain detailed information about the explosion of a star and provide significant scientific insight for a variety of fields because of the extreme conditions found within. Supernovae in our galaxy are not only rare on a human timescale but also happen at unscheduled times, so it is crucial to be ready and use all available instruments to capture all possible information from the event. The first indication of a potential stellar explosion will be the arrival of a bright burst of neutrinos. Its observation by multiple detectors worldwide can provide an early warning for the subsequent electromagnetic fireworks, as well as signal to other detectors with significant backgrounds so they can store their recent data. The supernova early warning system (SNEWS) has been operating as a simple coincidence between neutrino experiments in automated mode since 2005. In the current era of multi-messenger astronomy there are new opportunities for SNEWS to optimize sensitivity to science from the next galactic supernova beyond the simple early alert. This document is the product of a workshop in June 2019 towards design of SNEWS 2.0, an upgraded SNEWS with enhanced capabilities exploiting the unique advantages of prompt neutrino detection to maximize the science gained from such a valuable event.
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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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