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Real, D., Sanchez Losa, A., Diaz, A., Salesa Greus, F., & Calvo, D. (2023). The Neutrino Mediterranean Observatory Laser Beacon: Design and Qualification. Appl. Sci.-Basel, 13(17), 9935–16pp.
Abstract: This paper encapsulates details of the NEMO laser beacon's design, offering a profound contribution to the field of the time calibration of underwater neutrino telescopes. The mechanical design of the laser beacon, which operates at a depth of 3500 m, is presented, together with the design of the antibiofouling system employed to endure the operational pressure and optimize the operational range, enhancing its functionality and enabling time calibration among multiple towers. A noteworthy innovation central to this development lies in the battery system. This configuration enhances the device's portability, a crucial aspect in underwater operations. The comprehensive design of the laser beacon, encompassing the container housing, the requisite battery system for operation, electronics, and an effective antibiofouling system, is described in this paper. Additionally, this paper presents the findings of the laser beacon's qualification process.
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Fougeres, C. et al, & Domingo-Pardo, C. (2023). Search for Na-22 in novae supported by a novel method for measuring femtosecond nuclear lifetimes. Nat. Commun., 14(1), 4536–7pp.
Abstract: Classical novae are thermonuclear explosions in stellar binary systems, and important sources of Al-26 and Na-22. While ? rays from the decay of the former radioisotope have been observed throughout the Galaxy, Na-22 remains untraceable. Its half-life (2.6 yr) would allow the observation of its 1.275 MeV ?-ray line from a cosmic source. However, the prediction of such an observation requires good knowledge of its nucleosynthesis. The Na-22(p, ?)Mg-23 reaction remains the only source of large uncertainty about the amount of Na-22 ejected. Its rate is dominated by a single resonance on the short-lived state at 7785.0(7) keV in Mg-23. Here, we propose a combined analysis of particle-particle correlations and velocity-difference profiles to measure femtosecond nuclear lifetimes. The application of this method to the study of the Mg-23 states, places strong limits on the amount of Na-22 produced in novae and constrains its detectability with future space-borne observatories. The authors report a particle-particle correlation and velocity-difference profile method to measure nuclear lifetime. The results obtained for excited states of 23Mg are used to constrain the production of 22Na in the astrophysical novae explosions.
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Davesne, D., Pastore, A., & Navarro, J. (2023). Hartree-Fock Calculations in Semi-Infinite Matter with Gogny Interactions. Universe, 9(9), 398–11pp.
Abstract: Hartree-Fock equations in semi-infinite nuclear matter for finite range Gogny interactions are presented together with a detailed numerical scheme to solve them. The value of the surface energy is then extracted and given for standard Gogny interactions.
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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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Conde, D., Castillo, F. L., Escobar, C., García, C., Garcia Navarro, J. E., Sanz, V., et al. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather, 21(11), e2023SW003474–27pp.
Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.
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