|
|
Carrio, F., Castillo Gimenez, V., Ferrer, A., Gonzalez, V., Higon-Rodriguez, E., Marin, C., et al. (2011). Optical Link Card Design for the Phase II Upgrade of TileCal Experiment. IEEE Trans. Nucl. Sci., 58(4), 1657–1663.
Abstract: This paper presents the design of an optical link card developed in the frame of the R&D activities for the phase 2 upgrade of the TileCal experiment. This board, that is part of the evaluation of different technologies for the final choice in the next years, is designed as a mezzanine that can work independently or be plugged in the optical multiplexer board of the TileCal backend electronics. It includes two SNAP 12 optical connectors able to transmit and receive up to 75 Gb/s and one SFP optical connector for lower speeds and compatibility with existing hardware as the read out driver. All processing is done in a Stratix II GX field-programmable gate array (FPGA). Details are given on the hardware design, including signal and power integrity analysis, needed when working with these high data rates and on firmware development to obtain the best performance of the FPGA signal transceivers and for the use of the GBT protocol.
|
|
|
|
Cases, R., Ros, E., & Zuñiga, J. (2011). Measuring radon concentration in air using a diffusion cloud chamber. Am. J. Phys., 79(9), 903–908.
Abstract: Radon concentration in air is a major concern in lung cancer studies. A traditional technique used to measure radon abundance is the charcoal canister method. We propose a novel technique using a diffusion cloud chamber. This technique is simpler and can easily be used for physics demonstrations for high school and university students.
|
|
|
|
Chen, P., Ding, G. J., Rojas, A. D., Vaquera-Araujo, C. A., & Valle, J. W. F. (2016). Warped flavor symmetry predictions for neutrino physics. J. High Energy Phys., 01(1), 007–27pp.
Abstract: A realistic five-dimensional warped scenario with all standard model fields propagating in the bulk is proposed. Mass hierarchies would in principle be accounted for by judicious choices of the bulk mass parameters, while fermion mixing angles are restricted by a Delta(27) flavor symmetry broken on the branes by flavon fields.The latter gives stringent predictions for the neutrino mixing parameters, and the Dirac CP violation phase, all described in terms of only two independent parameters at leading order. The scheme also gives an adequate CKM fit and should be testable within upcoming oscillation experiments.
|
|
|
|
Choi, K. Y., Lopez-Fogliani, D. E., Muñoz, C., & Ruiz de Austri, R. (2010). Gamma-ray detection from gravitino dark matter decay in the μnu SSM. J. Cosmol. Astropart. Phys., 03(3), 028–14pp.
Abstract: The μnu SSM provides a solution to the mu-problem of the MSSM and explains the origin of neutrino masses by simply using right-handed neutrino superfields. Given that R-parity is broken in this model, the gravitino is a natural candidate for dark matter since its lifetime becomes much longer than the age of the Universe. We consider the implications of gravitino dark matter in the μnu SSM, analyzing in particular the prospects for detecting gamma rays from decaying gravitinos. If the gravitino explains the whole dark matter component, a gravitino mass larger than 20 GeV is disfavored by the isotropic diffuse photon background measurements. On the other hand, a gravitino with a mass range between 0.1 – 20 GeV gives rise to a signal that might be observed by the FERMI satellite. In this way important regions of the parameter space of the μnu SSM can be checked.
|
|
|
|
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.
|
|
