DUNE Collaboration(Abud, A. A. et al), Antonova, M., Barenboim, G., Cervera-Villanueva, A., De Romeri, V., Fernandez Menendez, P., et al. (2022). Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network. Eur. Phys. J. C, 82(10), 903–19pp.
Abstract: Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
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Rahaman, U., & Raut, S. K. (2022). On the tension between the latest NO nu A and T2K data. Eur. Phys. J. C, 82(10), 910–15pp.
Abstract: The latest data from the T2K and NO nu A experiments show a tension in their preferred values of the oscillation parameters. In this work, we try to identify the source of the tension between the data from these two experiments. An analysis of their data from various channels (individually, and combined) shows that the tension arises primarily from the nu(e) appearance data, and is compounded by the (nu) over bar (mu) disappearance data. We provide an explanation for the tension based on parameter degeneracies. Apart from the analysis with the standard matter effect, we also analyse the data with the vacuum oscillation hypothesis. We find that vacuum oscillations fit the data as well as matter effects do; and also reduce the tension between the two experiments. We have also done a study of the future run of NO nu A, T2K and DUNE in the context of establishing this tensionwith higher statistical significance.
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Botella, F. J., Cornet-Gomez, F., Miro, C., & Nebot, M. (2022). Muon and electron g-2 anomalies in a flavor conserving 2HDM with an oblique view on the CDF M-W value. Eur. Phys. J. C, 82(10), 915–25pp.
Abstract: We consider a type I or type X two Higgs doublets model with a modified lepton sector. The generalized lepton sector is also flavor conserving but with the new Yukawa couplings completely decoupled from lepton mass proportionality. The model is one loop stable under renormalization group evolution and it allows to reproduce the g – 2 muon anomaly together with the different scenarios one can consider for the electron g – 2 anomaly, related to the Cesium and/or to the Rubidium recoilmeasurements of the fine structure constant. Thorough parameter space analyses are performed to constrain all the model parameters in the different scenarios, either including or not including the recent CDF measurement of the W boson mass. For light new scalars with masses in the 0.2-1.0 TeV range, the muon anomaly receives dominant one loop contributions; it is for heavy new scalars with masses above 1.2 TeV that two loop Barr-Zee diagrams are needed. The electron g-2 anomaly, if any, must always be obtained with the two loop contributions. The final allowed regions are quite sensitive to the assumptions about perturbativity of Yukawa couplings, which influence unexpected observables like the allowed scalar mass ranges. On that respect, intermediate scalar masses, highly constrained by direct LHC searches, are allowed provided that the new lepton Yukawa couplings are fully scrutinized, including values up to 250 GeV. In the framework of a complete model, fully numerically analysed, we show the implications of the recent M-W measurement.
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Bandyopadhyay, P., Karan, A., Mandal, R., & Parashar, S. (2022). Distinguishing signatures of scalar leptoquarks at hadron and muon colliders. Eur. Phys. J. C, 82(10), 916–41pp.
Abstract: While the hunt for new states beyond the standardmodel (SM) goes on for various well motivated theories, the leptoquarks are among the most appealing scenarios at recent times due to a series of tensions observed in B-meson decays. We consider SU(2) singlet and triplet scalar leptoquarks separately, which contribute to charged and neutral current B-meson decays. Focusing on the single production of these two scalar leptoquarks, we perform a PYTHIA-based simulation considering all the dominant SM backgrounds at the current and future setups of the Large Hadron Collider (LHC). The mono-b-jet + p(T) finalstate gives the strongest signal for the singlet leptoquark at the 30 TeV LHC or Future Circular Collider (FCC), with a possibility of 5 sigma signal significance with greater than or similar to 1000 fb(-1) of integrated luminosity, for the chosen benchmark scenarios. The finalstate consisting of a c-jet and two tau-jets provides highest reach for the singlet leptoquark, probing an O(10(-1)) value of the Yukawa-type couplings for up to 3.0 TeV leptoquark mass. For the triplet leptoquark, 1 – jet + 2 μ+ p(T) topology is the most optimistic signature at the LHC, probing leptoquark couplings to fermions at O(10(-1)) value for the leptoquark mass range up to 4.0 TeV. The invariant mass edge distribution is found to be instrumental in determination of the leptoquarkmass scale at the LHC. We also perform the analysis at the proposed multiTeV muon collider, where an O(10(-1)) leptoquark Yukawa coupling can be probed for a 5.0 TeV leptoquark mass.
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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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