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Hernandez, P. (2012). CP violation in the neutrino sector: The new frontier. C. R. Phys., 13(2), 186–192.
Abstract: The discovery of neutrino masses has revealed a new flavour sector in the Standard Model. Just like the quark flavour sector, it contains a seed of CP violation, resulting in an asymmetric behaviour of matter and antimatter. It is argued that this new source of leptonic CP violation may be discovered in more precise neutrino oscillation experiments involving neutrino beams with energies in the GeV range that will be sent to distances of a few thousand kilometres.
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Gersabeck, E., & Pich, A. (2020). Tau and charm decays. C. R. Phys., 21(1), 75–92.
Abstract: A summary of recent precise results in tau and charm physics is presented. Topics include leptonic and hadronic tau decays, lepton flavour and lepton number violation, charm mixing and CP violation, leptonic and semileptonic charm decays, rare decays and spectroscopy.
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Fernandez Casani, A., Garcia Montoro, C., Gonzalez de la Hoz, S., Salt, J., Sanchez, J., & Villaplana Perez, M. (2023). Big Data Analytics for the ATLAS EventIndex Project with Apache Spark. Comput. Math. Methods, 2023, 6900908–19pp.
Abstract: The ATLAS EventIndex was designed to provide a global event catalogue and limited event-level metadata for ATLAS experiment of the Large Hadron Collider (LHC) and their analysis groups and users during Run 2 (2015-2018) and has been running in production since. The LHC Run 3, started in 2022, has seen increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. A new core storage service is being developed in HBase/Phoenix, and there is work in progress to provide at least the same functionality as the current one for increased data ingestion and search rates and with increasing volumes of stored data. In addition, new tools are being developed for solving the needed access cases within the new storage. This paper describes a new tool using Spark and implemented in Scala for accessing the big data quantities of the EventIndex project stored in HBase/Phoenix. With this tool, we can offer data discovery capabilities at different granularities, providing Spark Dataframes that can be used or refined within the same framework. Data analytic cases of the EventIndex project are implemented, like the search for duplicates of events from the same or different datasets. An algorithm and implementation for the calculation of overlap matrices of events across different datasets are presented. Our approach can be used by other higher-level tools and users, to ease access to the data in a performant and standard way using Spark abstractions. The provided tools decouple data access from the actual data schema, which makes it convenient to hide complexity and possible changes on the backed storage.
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Ferrer-Sanchez, A., Martin-Guerrero, J., Ruiz de Austri, R., Torres-Forne, A., & Font, J. A. (2024). Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics. Comput. Meth. Appl. Mech. Eng., 424, 116906–18pp.
Abstract: We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions.
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Basso, L., Belyaev, A., Chowdhury, D., Hirsch, M., Khalil, S., Moretti, S., et al. (2013). Proposal for generalised supersymmetry Les Houches Accord for see-saw models and PDG numbering scheme. Comput. Phys. Commun., 184(3), 698–719.
Abstract: The SUSY Les Houches Accord (SLHA) 2 extended the first SLHA to include various generalisations of the Minimal Supersymmetric Standard Model (MSSM) as well as its simplest next-to-minimal version. Here, we propose further extensions to it, to include the most general and well-established see-saw descriptions (types I/II/III, inverse, and linear) in both an effective and a simple gauged extension of the MSSM framework. In addition, we generalise the PDG numbering scheme to reflect the properties of the particles. (c) 2012 Elsevier B.V. All rights reserved.
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