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Bennett, J. J., Buldgen, G., de Salas, P. F., Drewes, M., Gariazzo, S., Pastor, S., et al. (2021). Towards a precision calculation of the effective number of neutrinos N-eff in the Standard Model. Part II. Neutrino decoupling in the presence of flavour oscillations and finite-temperature QED. J. Cosmol. Astropart. Phys., 04(4), 073–33pp.
Abstract: We present in this work a new calculation of the standard-model benchmark value for the effective number of neutrinos, N-eff(SM), that quantifies the cosmological neutrinoto-photon energy densities. The calculation takes into account neutrino flavour oscillations, finite-temperature effects in the quantum electrodynamics plasma to O(e(3)), where e is the elementary electric charge, and a full evaluation of the neutrino-neutrino collision integral. We provide furthermore a detailed assessment of the uncertainties in the benchmark N(eff)(SM )value, through testing the value's dependence on (i) optional approximate modelling of the weak collision integrals, (ii) measurement errors in the physical parameters of the weak sector, and (iii) numerical convergence, particularly in relation to momentum discretisation. Our new, recommended standard-model benchmark is N-eff(SM) 3.0440 +/- 0.0002, where the nominal uncertainty is attributed predominantly to errors incurred in the numerical solution procedure (vertical bar delta N-eff vertical bar similar to 10(-4)), augmented by measurement errors in the solar mixing angle sin(2) theta(12) (vertical bar delta N-eff vertical bar similar to 10(-4)).
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LHCb Collaboration(Aaij, R. et al), Henry, L., Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., et al. (2021). Evidence of a J/psi Lambda structure and observation of excited Xi(-) states in the Xi(-)(b) -> J/psi Lambda K- decay. Sci. Bull., 66(13), 1278–1287.
Abstract: First evidence of a structure in the J/psi Lambda invariant mass distribution is obtained from an amplitude analysis of Xi(-)(b) -> J/psi Lambda K- decays. The observed structure is consistent with being due to a charmonium pentaquark with strangeness with a significance of 3.1r including systematic uncertainties and lookelsewhere effect. Its mass and width are determined to be 4458.8 +/- 2.9(-1.1)(+4.7) MeV and 17.3 +/- 6.5(-5.7)(+8.0) MeV, respectively, where the quoted uncertainties are statistical and systematic. The structure is also consistent with being due to two resonances. In addition, the narrow excited Xi(-) states, Xi(-)(1690) and Xi(-)(1820)(-), are seen for the first time in a Xi(-)(b) decay, and their masses and widths are measured with improved precision. The analysis is performed using pp collision data corresponding to a total integrated luminosity of 9 fb(-1), collected with the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV.
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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2021). Search for the doubly charmed baryon Omega(+)(cc). Sci. China-Phys. Mech. Astron., 64(10), 101062–12pp.
Abstract: A search for the doubly charmed baryon Omega(+)(cc) with the decay mode Omega(+)(cc) -> Xi K-+(c)-pi(+) is performed using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment from 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb(-1). No significant signal is observed within the invariant mass range of 3.6 to 4.0GeV/c(2). Upper limits are set on the ratio R of the production cross-section times the total branching fraction of the Omega(+)(cc) -> Xi K-+(c)-pi(+) decay with respect to the Xi(++)(cc) -> Lambda K-+(c)-pi(+)pi(+) decay. Upper limits at 95% credibility level for R in the range 0.005 to 0.11 are obtained for different hypotheses on the Omega(+)(cc) mass and lifetime in the rapidity range from 2.0 to 4.5 and transverse momentum range from 4 to 15 GeV/c.
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Salesa Greus, F., & Sanchez Losa, A. (2021). Multimessenger Astronomy with Neutrinos. Universe, 7(11), 397–11pp.
Abstract: Multimessenger astronomy is arguably the branch of the astroparticle physics field that has seen the most significant developments in recent years. In this manuscript, we will review the state-of-the-art, the recent observations, and the prospects and challenges for the near future. We will give special emphasis to the observation carried out with neutrino telescopes.
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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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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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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Measurement of prompt charged-particle production in pp collisions at root s=13 TeV. J. High Energy Phys., 01(1), 166–39pp.
Abstract: The differential cross-section of prompt inclusive production of long-lived charged particles in proton-proton collisions is measured using a data sample recorded by the LHCb experiment at a centre-of-mass energy of root s = 13 TeV. The data sample, collected with an unbiased trigger, corresponds to an integrated luminosity of 5.4 nb(-1). The differential cross-section is measured as a function of transverse momentum and pseudorapidity in the ranges P-T is an element of [80, 10 000) MeV/c and eta is an element of [2.0, 4.8) and is determined separately for positively and negatively charged particles. The results are compared with predictions from various hadronic-interaction models.
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Borsato, M. et al, Zurita, J., Henry, L., Jashal, B. K., & Oyanguren, A. (2022). Unleashing the full power of LHCb to probe stealth new physics. Rep. Prog. Phys., 85(2), 024201–45pp.
Abstract: In this paper, we describe the potential of the LHCb experiment to detect stealth physics. This refers to dynamics beyond the standard model that would elude searches that focus on energetic objects or precision measurements of known processes. Stealth signatures include long-lived particles and light resonances that are produced very rarely or together with overwhelming backgrounds. We will discuss why LHCb is equipped to discover this kind of physics at the Large Hadron Collider and provide examples of well-motivated theoretical models that can be probed with great detail at the experiment.
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Hirn, J., Garcia, J. E., Montesinos-Navarro, A., Sanchez-Martin, R., Sanz, V., & Verdu, M. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods Ecol. Evol., 13, 1052–1061.
Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
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ATLAS Collaboration(Aad, G. et al), Amos, K. R., Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Cardillo, F., et al. (2022). Operation and performance of the ATLAS semiconductor tracker in LHC Run 2. J. Instrum., 17(1), P01013–56pp.
Abstract: The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules. During Run 2 (2015-2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb(-1) to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector. Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2. It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%. Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules. '
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